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  <div class="headertitle"><div class="title">MappedTensor.cu</div></div>
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<div class="fragment"><div class="line"><a id="l00001" name="l00001"></a><span class="lineno">    1</span><span class="preprocessor">#include &quot;NeuZephyr/MappedTensor.cuh&quot;</span></div>
<div class="line"><a id="l00002" name="l00002"></a><span class="lineno">    2</span> </div>
<div class="line"><a id="l00003" name="l00003"></a><span class="lineno">    3</span><span class="preprocessor">#include &lt;chrono&gt;</span></div>
<div class="line"><a id="l00004" name="l00004"></a><span class="lineno">    4</span><span class="preprocessor">#include &lt;iterator&gt;</span></div>
<div class="line"><a id="l00005" name="l00005"></a><span class="lineno">    5</span><span class="preprocessor">#include &lt;iostream&gt;</span></div>
<div class="line"><a id="l00006" name="l00006"></a><span class="lineno">    6</span><span class="preprocessor">#include &lt;curand.h&gt;</span></div>
<div class="line"><a id="l00007" name="l00007"></a><span class="lineno">    7</span> </div>
<div class="line"><a id="l00008" name="l00008"></a><span class="lineno">    8</span><span class="preprocessor">#include &quot;NeuZephyr/NeuZephyrCudaErrorHandling.cuh&quot;</span></div>
<div class="line"><a id="l00009" name="l00009"></a><span class="lineno">    9</span><span class="preprocessor">#include &quot;<a class="code" href="_operation_kernels_8cuh.html">NeuZephyr/OperationKernels.cuh</a>&quot;</span></div>
<div class="line"><a id="l00010" name="l00010"></a><span class="lineno">   10</span><span class="preprocessor">#include &quot;NeuZephyr/utils.cuh&quot;</span></div>
<div class="line"><a id="l00011" name="l00011"></a><span class="lineno">   11</span><span class="preprocessor">#include &quot;NeuZephyr/StreamManager.cuh&quot;</span></div>
<div class="line"><a id="l00012" name="l00012"></a><span class="lineno">   12</span><span class="preprocessor">#include &quot;NeuZephyr/TensorOperations.cuh&quot;</span></div>
<div class="line"><a id="l00013" name="l00013"></a><span class="lineno">   13</span> </div>
<div class="line"><a id="l00014" name="l00014"></a><span class="lineno">   14</span><span class="keyword">namespace </span><a class="code hl_namespace" href="namespacenz_1_1data.html">nz::data</a> {</div>
<div class="foldopen" id="foldopen00045" data-start="{" data-end="};">
<div class="line"><a id="l00045" name="l00045"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_mapped_tensor.html#a1cd02ca3d7e7592ba6658b333ff207b4">   45</a></span>    std::ostream&amp; operator&lt;&lt;(std::ostream&amp; os, <span class="keyword">const</span> <a class="code hl_class" href="classnz_1_1data_1_1_mapped_tensor.html">MappedTensor</a>&amp; tensor) {</div>
<div class="line"><a id="l00046" name="l00046"></a><span class="lineno">   46</span>        tensor.<a class="code hl_function" href="classnz_1_1data_1_1_mapped_tensor.html#a8a42265d7c9f964c41ff43e9d8dc7bf7">print</a>(os);</div>
<div class="line"><a id="l00047" name="l00047"></a><span class="lineno">   47</span>        <span class="keywordflow">if</span> (tensor._requires_grad) {</div>
<div class="line"><a id="l00048" name="l00048"></a><span class="lineno">   48</span>            os &lt;&lt; <span class="stringliteral">&quot;Gradient: &quot;</span> &lt;&lt; std::endl;</div>
<div class="line"><a id="l00049" name="l00049"></a><span class="lineno">   49</span>            tensor.<a class="code hl_function" href="classnz_1_1data_1_1_mapped_tensor.html#ab54e80ac0d2ec90442b0a674004cfdbc">printGrad</a>(os);</div>
<div class="line"><a id="l00050" name="l00050"></a><span class="lineno">   50</span>        }</div>
<div class="line"><a id="l00051" name="l00051"></a><span class="lineno">   51</span>        <span class="keywordflow">return</span> os;</div>
<div class="line"><a id="l00052" name="l00052"></a><span class="lineno">   52</span>    }</div>
</div>
<div class="line"><a id="l00053" name="l00053"></a><span class="lineno">   53</span> </div>
<div class="foldopen" id="foldopen00081" data-start="{" data-end="};">
<div class="line"><a id="l00081" name="l00081"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_mapped_tensor.html#abaacd16c39a6f16fde32336c7696c94f">   81</a></span>    std::istream&amp; operator&gt;&gt;(std::istream&amp; is, <a class="code hl_class" href="classnz_1_1data_1_1_mapped_tensor.html">MappedTensor</a>&amp; tensor) {</div>
<div class="line"><a id="l00082" name="l00082"></a><span class="lineno">   82</span>        MappedTensor::size_type i = 0;</div>
<div class="line"><a id="l00083" name="l00083"></a><span class="lineno">   83</span>        <span class="keywordflow">while</span> (i &lt; tensor._size &amp;&amp; is &gt;&gt; tensor._data[i++]) {</div>
<div class="line"><a id="l00084" name="l00084"></a><span class="lineno">   84</span>        }</div>
<div class="line"><a id="l00085" name="l00085"></a><span class="lineno">   85</span>        <span class="keywordflow">return</span> is;</div>
<div class="line"><a id="l00086" name="l00086"></a><span class="lineno">   86</span>    }</div>
</div>
<div class="line"><a id="l00087" name="l00087"></a><span class="lineno">   87</span> </div>
<div class="foldopen" id="foldopen00088" data-start="{" data-end="}">
<div class="line"><a id="l00088" name="l00088"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_mapped_tensor.html#a8534e7ba30e0eaab0921ebb9632c5fbd">   88</a></span>    <a class="code hl_function" href="classnz_1_1data_1_1_mapped_tensor.html#a54475a46dfd208a2ee8c1e403535abc8">MappedTensor::MappedTensor</a>(<span class="keyword">const</span> <a class="code hl_class" href="classnz_1_1data_1_1_dimension.html">shape_type</a>&amp; shape, <span class="keywordtype">bool</span> requires_grad) : _shape(shape),</div>
<div class="line"><a id="l00089" name="l00089"></a><span class="lineno">   89</span>                                                                              _size(shape.size()),</div>
<div class="line"><a id="l00090" name="l00090"></a><span class="lineno">   90</span>                                                                              _requires_grad(requires_grad) {</div>
<div class="line"><a id="l00091" name="l00091"></a><span class="lineno">   91</span>        CHECK(cudaMallocHost(&amp;_data, _size * <span class="keyword">sizeof</span>(value_type)));</div>
<div class="line"><a id="l00092" name="l00092"></a><span class="lineno">   92</span>        <span class="keywordflow">if</span> (requires_grad) {</div>
<div class="line"><a id="l00093" name="l00093"></a><span class="lineno">   93</span>            CHECK(cudaMallocHost(&amp;_grad, _size * <span class="keyword">sizeof</span>(value_type)));</div>
<div class="line"><a id="l00094" name="l00094"></a><span class="lineno">   94</span>        }</div>
<div class="line"><a id="l00095" name="l00095"></a><span class="lineno">   95</span>        <span class="keywordflow">else</span> {</div>
<div class="line"><a id="l00096" name="l00096"></a><span class="lineno">   96</span>            _grad = <span class="keyword">nullptr</span>;</div>
<div class="line"><a id="l00097" name="l00097"></a><span class="lineno">   97</span>        }</div>
<div class="line"><a id="l00098" name="l00098"></a><span class="lineno">   98</span>    }</div>
</div>
<div class="line"><a id="l00099" name="l00099"></a><span class="lineno">   99</span> </div>
<div class="foldopen" id="foldopen00100" data-start="{" data-end="}">
<div class="line"><a id="l00100" name="l00100"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_mapped_tensor.html#a54475a46dfd208a2ee8c1e403535abc8">  100</a></span>    <a class="code hl_function" href="classnz_1_1data_1_1_mapped_tensor.html#a54475a46dfd208a2ee8c1e403535abc8">MappedTensor::MappedTensor</a>() : <a class="code hl_class" href="classnz_1_1data_1_1_mapped_tensor.html">MappedTensor</a>({0, 0, 0, 0}, <span class="keyword">false</span>) {</div>
<div class="line"><a id="l00101" name="l00101"></a><span class="lineno">  101</span>        _data = <span class="keyword">nullptr</span>;</div>
<div class="line"><a id="l00102" name="l00102"></a><span class="lineno">  102</span>        _grad = <span class="keyword">nullptr</span>;</div>
<div class="line"><a id="l00103" name="l00103"></a><span class="lineno">  103</span>    }</div>
</div>
<div class="line"><a id="l00104" name="l00104"></a><span class="lineno">  104</span> </div>
<div class="foldopen" id="foldopen00105" data-start="{" data-end="}">
<div class="line"><a id="l00105" name="l00105"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_mapped_tensor.html#a83cc3f2c2b973e10cea67c234761241d">  105</a></span>    <a class="code hl_function" href="classnz_1_1data_1_1_mapped_tensor.html#a54475a46dfd208a2ee8c1e403535abc8">MappedTensor::MappedTensor</a>(<span class="keyword">const</span> <a class="code hl_class" href="classnz_1_1data_1_1_mapped_tensor.html">MappedTensor</a>&amp; other) : <a class="code hl_class" href="classnz_1_1data_1_1_mapped_tensor.html">MappedTensor</a>(other._shape, other._requires_grad) {</div>
<div class="line"><a id="l00106" name="l00106"></a><span class="lineno">  106</span>        <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#afa38d5c6db0e6b48c8f74ce8ad0df2bc">memcpy</a>(_data, other._data, _size * <span class="keyword">sizeof</span>(value_type),</div>
<div class="line"><a id="l00107" name="l00107"></a><span class="lineno">  107</span>                                                             cudaMemcpyDeviceToDevice);</div>
<div class="line"><a id="l00108" name="l00108"></a><span class="lineno">  108</span>        <span class="keywordflow">if</span> (_requires_grad) {</div>
<div class="line"><a id="l00109" name="l00109"></a><span class="lineno">  109</span>            <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#afa38d5c6db0e6b48c8f74ce8ad0df2bc">memcpy</a>(_grad, other._grad, _size * <span class="keyword">sizeof</span>(value_type),</div>
<div class="line"><a id="l00110" name="l00110"></a><span class="lineno">  110</span>                                                                 cudaMemcpyDeviceToDevice);</div>
<div class="line"><a id="l00111" name="l00111"></a><span class="lineno">  111</span>        }</div>
<div class="line"><a id="l00112" name="l00112"></a><span class="lineno">  112</span>    }</div>
</div>
<div class="line"><a id="l00113" name="l00113"></a><span class="lineno">  113</span> </div>
<div class="foldopen" id="foldopen00114" data-start="{" data-end="}">
<div class="line"><a id="l00114" name="l00114"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_mapped_tensor.html#a2bff8300e61805bbb3e480417aab3ae8">  114</a></span>    <a class="code hl_function" href="classnz_1_1data_1_1_mapped_tensor.html#a54475a46dfd208a2ee8c1e403535abc8">MappedTensor::MappedTensor</a>(<a class="code hl_class" href="classnz_1_1data_1_1_mapped_tensor.html">MappedTensor</a>&amp;&amp; other) <span class="keyword">noexcept</span> {</div>
<div class="line"><a id="l00115" name="l00115"></a><span class="lineno">  115</span>        _shape = other._shape;</div>
<div class="line"><a id="l00116" name="l00116"></a><span class="lineno">  116</span>        _size = other._size;</div>
<div class="line"><a id="l00117" name="l00117"></a><span class="lineno">  117</span>        _requires_grad = other._requires_grad;</div>
<div class="line"><a id="l00118" name="l00118"></a><span class="lineno">  118</span>        _data = other._data;</div>
<div class="line"><a id="l00119" name="l00119"></a><span class="lineno">  119</span>        _grad = other._grad;</div>
<div class="line"><a id="l00120" name="l00120"></a><span class="lineno">  120</span>        other._data = <span class="keyword">nullptr</span>;</div>
<div class="line"><a id="l00121" name="l00121"></a><span class="lineno">  121</span>        other._grad = <span class="keyword">nullptr</span>;</div>
<div class="line"><a id="l00122" name="l00122"></a><span class="lineno">  122</span>        other._size = 0;</div>
<div class="line"><a id="l00123" name="l00123"></a><span class="lineno">  123</span>        other._requires_grad = <span class="keyword">false</span>;</div>
<div class="line"><a id="l00124" name="l00124"></a><span class="lineno">  124</span>        other._shape = {0, 0, 0, 0};</div>
<div class="line"><a id="l00125" name="l00125"></a><span class="lineno">  125</span>    }</div>
</div>
<div class="line"><a id="l00126" name="l00126"></a><span class="lineno">  126</span> </div>
<div class="foldopen" id="foldopen00127" data-start="{" data-end="}">
<div class="line"><a id="l00127" name="l00127"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_mapped_tensor.html#aaf2087390e5524c8ff6790bd94c78e90">  127</a></span>    <a class="code hl_class" href="classnz_1_1data_1_1_mapped_tensor.html">MappedTensor</a>&amp; <a class="code hl_function" href="classnz_1_1data_1_1_mapped_tensor.html#aaf2087390e5524c8ff6790bd94c78e90">MappedTensor::operator=</a>(<span class="keyword">const</span> <a class="code hl_class" href="classnz_1_1data_1_1_mapped_tensor.html">MappedTensor</a>&amp; other) {</div>
<div class="line"><a id="l00128" name="l00128"></a><span class="lineno">  128</span>        <span class="keywordflow">if</span> (<span class="keyword">this</span> != &amp;other) {</div>
<div class="line"><a id="l00129" name="l00129"></a><span class="lineno">  129</span>            <span class="keywordflow">if</span> (_requires_grad &amp;&amp; _grad) {</div>
<div class="line"><a id="l00130" name="l00130"></a><span class="lineno">  130</span>                <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab6803232b9c08d9282b16322a6c7b8a9">freeHost</a>(_grad);</div>
<div class="line"><a id="l00131" name="l00131"></a><span class="lineno">  131</span>            }</div>
<div class="line"><a id="l00132" name="l00132"></a><span class="lineno">  132</span>            <span class="keywordflow">if</span> (_data) {</div>
<div class="line"><a id="l00133" name="l00133"></a><span class="lineno">  133</span>                <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab6803232b9c08d9282b16322a6c7b8a9">freeHost</a>(_data);</div>
<div class="line"><a id="l00134" name="l00134"></a><span class="lineno">  134</span>            }</div>
<div class="line"><a id="l00135" name="l00135"></a><span class="lineno">  135</span>            _shape = other._shape;</div>
<div class="line"><a id="l00136" name="l00136"></a><span class="lineno">  136</span>            _size = other._size;</div>
<div class="line"><a id="l00137" name="l00137"></a><span class="lineno">  137</span>            _requires_grad = other._requires_grad;</div>
<div class="line"><a id="l00138" name="l00138"></a><span class="lineno">  138</span>            CHECK(cudaMallocHost(&amp;_data, _size * <span class="keyword">sizeof</span>(value_type)));</div>
<div class="line"><a id="l00139" name="l00139"></a><span class="lineno">  139</span>            <span class="keywordflow">if</span> (_requires_grad) {</div>
<div class="line"><a id="l00140" name="l00140"></a><span class="lineno">  140</span>                CHECK(cudaMallocHost(&amp;_grad, _size * <span class="keyword">sizeof</span>(value_type)));</div>
<div class="line"><a id="l00141" name="l00141"></a><span class="lineno">  141</span>            }</div>
<div class="line"><a id="l00142" name="l00142"></a><span class="lineno">  142</span>            <span class="keywordflow">else</span> {</div>
<div class="line"><a id="l00143" name="l00143"></a><span class="lineno">  143</span>                _grad = <span class="keyword">nullptr</span>;</div>
<div class="line"><a id="l00144" name="l00144"></a><span class="lineno">  144</span>            }</div>
<div class="line"><a id="l00145" name="l00145"></a><span class="lineno">  145</span>            <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#afa38d5c6db0e6b48c8f74ce8ad0df2bc">memcpy</a>(_data, other._data, _size * <span class="keyword">sizeof</span>(value_type),</div>
<div class="line"><a id="l00146" name="l00146"></a><span class="lineno">  146</span>                                                                 cudaMemcpyDeviceToDevice);</div>
<div class="line"><a id="l00147" name="l00147"></a><span class="lineno">  147</span>            <span class="keywordflow">if</span> (_requires_grad) {</div>
<div class="line"><a id="l00148" name="l00148"></a><span class="lineno">  148</span>                <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#afa38d5c6db0e6b48c8f74ce8ad0df2bc">memcpy</a>(_grad, other._grad, _size * <span class="keyword">sizeof</span>(value_type),</div>
<div class="line"><a id="l00149" name="l00149"></a><span class="lineno">  149</span>                                                                     cudaMemcpyDeviceToDevice);</div>
<div class="line"><a id="l00150" name="l00150"></a><span class="lineno">  150</span>            }</div>
<div class="line"><a id="l00151" name="l00151"></a><span class="lineno">  151</span>            <span class="keywordflow">return</span> *<span class="keyword">this</span>;</div>
<div class="line"><a id="l00152" name="l00152"></a><span class="lineno">  152</span>        }</div>
<div class="line"><a id="l00153" name="l00153"></a><span class="lineno">  153</span>        <span class="keywordflow">return</span> *<span class="keyword">this</span>;</div>
<div class="line"><a id="l00154" name="l00154"></a><span class="lineno">  154</span>    }</div>
</div>
<div class="line"><a id="l00155" name="l00155"></a><span class="lineno">  155</span> </div>
<div class="foldopen" id="foldopen00156" data-start="{" data-end="}">
<div class="line"><a id="l00156" name="l00156"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_mapped_tensor.html#aa3658f80db10a7715aa45d871ef49cdd">  156</a></span>    <a class="code hl_class" href="classnz_1_1data_1_1_mapped_tensor.html">MappedTensor</a>&amp; <a class="code hl_function" href="classnz_1_1data_1_1_mapped_tensor.html#aaf2087390e5524c8ff6790bd94c78e90">MappedTensor::operator=</a>(<a class="code hl_class" href="classnz_1_1data_1_1_mapped_tensor.html">MappedTensor</a>&amp;&amp; other) <span class="keyword">noexcept</span>(<span class="keyword">false</span>) {</div>
<div class="line"><a id="l00157" name="l00157"></a><span class="lineno">  157</span>        <span class="keywordflow">if</span> (<span class="keyword">this</span> != &amp;other) {</div>
<div class="line"><a id="l00158" name="l00158"></a><span class="lineno">  158</span>            <span class="keywordflow">if</span> (_data) {</div>
<div class="line"><a id="l00159" name="l00159"></a><span class="lineno">  159</span>                <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab6803232b9c08d9282b16322a6c7b8a9">freeHost</a>(_data);</div>
<div class="line"><a id="l00160" name="l00160"></a><span class="lineno">  160</span>            }</div>
<div class="line"><a id="l00161" name="l00161"></a><span class="lineno">  161</span>            <span class="keywordflow">if</span> (_requires_grad &amp;&amp; _grad) {</div>
<div class="line"><a id="l00162" name="l00162"></a><span class="lineno">  162</span>                <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab6803232b9c08d9282b16322a6c7b8a9">freeHost</a>(_grad);</div>
<div class="line"><a id="l00163" name="l00163"></a><span class="lineno">  163</span>            }</div>
<div class="line"><a id="l00164" name="l00164"></a><span class="lineno">  164</span>            _shape = other._shape;</div>
<div class="line"><a id="l00165" name="l00165"></a><span class="lineno">  165</span>            _size = other._size;</div>
<div class="line"><a id="l00166" name="l00166"></a><span class="lineno">  166</span>            _requires_grad = other._requires_grad;</div>
<div class="line"><a id="l00167" name="l00167"></a><span class="lineno">  167</span>            _data = other._data;</div>
<div class="line"><a id="l00168" name="l00168"></a><span class="lineno">  168</span>            _grad = other._grad;</div>
<div class="line"><a id="l00169" name="l00169"></a><span class="lineno">  169</span>            other._data = <span class="keyword">nullptr</span>;</div>
<div class="line"><a id="l00170" name="l00170"></a><span class="lineno">  170</span>            other._grad = <span class="keyword">nullptr</span>;</div>
<div class="line"><a id="l00171" name="l00171"></a><span class="lineno">  171</span>            other._size = 0;</div>
<div class="line"><a id="l00172" name="l00172"></a><span class="lineno">  172</span>            other._requires_grad = <span class="keyword">false</span>;</div>
<div class="line"><a id="l00173" name="l00173"></a><span class="lineno">  173</span>            other._shape = {0, 0, 0, 0};</div>
<div class="line"><a id="l00174" name="l00174"></a><span class="lineno">  174</span>            <span class="keywordflow">return</span> *<span class="keyword">this</span>;</div>
<div class="line"><a id="l00175" name="l00175"></a><span class="lineno">  175</span>        }</div>
<div class="line"><a id="l00176" name="l00176"></a><span class="lineno">  176</span>        <span class="keywordflow">return</span> *<span class="keyword">this</span>;</div>
<div class="line"><a id="l00177" name="l00177"></a><span class="lineno">  177</span>    }</div>
</div>
<div class="line"><a id="l00178" name="l00178"></a><span class="lineno">  178</span> </div>
<div class="foldopen" id="foldopen00179" data-start="{" data-end="}">
<div class="line"><a id="l00179" name="l00179"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_mapped_tensor.html#aee0ad19fbaa29f4a89e99f21bdd4cdf0">  179</a></span>    <a class="code hl_function" href="classnz_1_1data_1_1_mapped_tensor.html#aee0ad19fbaa29f4a89e99f21bdd4cdf0">MappedTensor::~MappedTensor</a>() noexcept(false) {</div>
<div class="line"><a id="l00180" name="l00180"></a><span class="lineno">  180</span>        <span class="keywordflow">if</span> (_requires_grad &amp;&amp; _grad) {</div>
<div class="line"><a id="l00181" name="l00181"></a><span class="lineno">  181</span>            <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab6803232b9c08d9282b16322a6c7b8a9">freeHost</a>(_grad);</div>
<div class="line"><a id="l00182" name="l00182"></a><span class="lineno">  182</span>        }</div>
<div class="line"><a id="l00183" name="l00183"></a><span class="lineno">  183</span>        <span class="keywordflow">if</span> (_data) {</div>
<div class="line"><a id="l00184" name="l00184"></a><span class="lineno">  184</span>            <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab6803232b9c08d9282b16322a6c7b8a9">freeHost</a>(_data);</div>
<div class="line"><a id="l00185" name="l00185"></a><span class="lineno">  185</span>        }</div>
<div class="line"><a id="l00186" name="l00186"></a><span class="lineno">  186</span>    }</div>
</div>
<div class="line"><a id="l00187" name="l00187"></a><span class="lineno">  187</span> </div>
<div class="foldopen" id="foldopen00188" data-start="{" data-end="}">
<div class="line"><a id="l00188" name="l00188"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_mapped_tensor.html#ad76dcba2cd22c53b8c4815da113dcb56">  188</a></span>    MappedTensor::iterator <a class="code hl_function" href="classnz_1_1data_1_1_mapped_tensor.html#ad76dcba2cd22c53b8c4815da113dcb56">MappedTensor::begin</a>()<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00189" name="l00189"></a><span class="lineno">  189</span>        <a class="code hl_function" href="classnz_1_1data_1_1_mapped_tensor.html#a5134b8821da18631dd9a9b9417b0ba5e">sync</a>();</div>
<div class="line"><a id="l00190" name="l00190"></a><span class="lineno">  190</span>        <span class="keywordflow">return</span> _data;</div>
<div class="line"><a id="l00191" name="l00191"></a><span class="lineno">  191</span>    }</div>
</div>
<div class="line"><a id="l00192" name="l00192"></a><span class="lineno">  192</span> </div>
<div class="foldopen" id="foldopen00193" data-start="{" data-end="}">
<div class="line"><a id="l00193" name="l00193"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_mapped_tensor.html#ac01f432f0ef9b7279630b5b35bc609e0">  193</a></span>    MappedTensor::iterator <a class="code hl_function" href="classnz_1_1data_1_1_mapped_tensor.html#ac01f432f0ef9b7279630b5b35bc609e0">MappedTensor::end</a>()<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00194" name="l00194"></a><span class="lineno">  194</span>        <a class="code hl_function" href="classnz_1_1data_1_1_mapped_tensor.html#a5134b8821da18631dd9a9b9417b0ba5e">sync</a>();</div>
<div class="line"><a id="l00195" name="l00195"></a><span class="lineno">  195</span>        <span class="keywordflow">return</span> _data + _size;</div>
<div class="line"><a id="l00196" name="l00196"></a><span class="lineno">  196</span>    }</div>
</div>
<div class="line"><a id="l00197" name="l00197"></a><span class="lineno">  197</span> </div>
<div class="foldopen" id="foldopen00198" data-start="{" data-end="}">
<div class="line"><a id="l00198" name="l00198"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_mapped_tensor.html#ad9c451d7aa04362e84e0d68d42a8867a">  198</a></span>    <span class="keywordtype">bool</span> <a class="code hl_function" href="classnz_1_1data_1_1_mapped_tensor.html#ad9c451d7aa04362e84e0d68d42a8867a">MappedTensor::requiresGrad</a>() const noexcept {</div>
<div class="line"><a id="l00199" name="l00199"></a><span class="lineno">  199</span>        <span class="keywordflow">return</span> _requires_grad;</div>
<div class="line"><a id="l00200" name="l00200"></a><span class="lineno">  200</span>    }</div>
</div>
<div class="line"><a id="l00201" name="l00201"></a><span class="lineno">  201</span> </div>
<div class="foldopen" id="foldopen00202" data-start="{" data-end="}">
<div class="line"><a id="l00202" name="l00202"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_mapped_tensor.html#ad72562ee8cad3196dc9ffbdd436f74a3">  202</a></span>    MappedTensor::value_type* <a class="code hl_function" href="classnz_1_1data_1_1_mapped_tensor.html#ad72562ee8cad3196dc9ffbdd436f74a3">MappedTensor::data</a>() const noexcept {</div>
<div class="line"><a id="l00203" name="l00203"></a><span class="lineno">  203</span>        <span class="keywordflow">return</span> _data;</div>
<div class="line"><a id="l00204" name="l00204"></a><span class="lineno">  204</span>    }</div>
</div>
<div class="line"><a id="l00205" name="l00205"></a><span class="lineno">  205</span> </div>
<div class="foldopen" id="foldopen00206" data-start="{" data-end="}">
<div class="line"><a id="l00206" name="l00206"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_mapped_tensor.html#a91ee08c2ba51db828c54fa63549bd293">  206</a></span>    MappedTensor::value_type* <a class="code hl_function" href="classnz_1_1data_1_1_mapped_tensor.html#a91ee08c2ba51db828c54fa63549bd293">MappedTensor::grad</a>()<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00207" name="l00207"></a><span class="lineno">  207</span>        <span class="keywordflow">if</span> (!_requires_grad) {</div>
<div class="line"><a id="l00208" name="l00208"></a><span class="lineno">  208</span>            <span class="keywordflow">throw</span> std::invalid_argument(<span class="stringliteral">&quot;Gradient access is not allowed for tensors that do not require gradients.&quot;</span>);</div>
<div class="line"><a id="l00209" name="l00209"></a><span class="lineno">  209</span>        }</div>
<div class="line"><a id="l00210" name="l00210"></a><span class="lineno">  210</span>        <span class="keywordflow">return</span> _grad;</div>
<div class="line"><a id="l00211" name="l00211"></a><span class="lineno">  211</span>    }</div>
</div>
<div class="line"><a id="l00212" name="l00212"></a><span class="lineno">  212</span> </div>
<div class="foldopen" id="foldopen00213" data-start="{" data-end="}">
<div class="line"><a id="l00213" name="l00213"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_mapped_tensor.html#aa144f9f3b76741e5d3fb3b5df3cc3b42">  213</a></span>    MappedTensor::size_type <a class="code hl_function" href="classnz_1_1data_1_1_mapped_tensor.html#aa144f9f3b76741e5d3fb3b5df3cc3b42">MappedTensor::size</a>() const noexcept {</div>
<div class="line"><a id="l00214" name="l00214"></a><span class="lineno">  214</span>        <span class="keywordflow">return</span> _size;</div>
<div class="line"><a id="l00215" name="l00215"></a><span class="lineno">  215</span>    }</div>
</div>
<div class="line"><a id="l00216" name="l00216"></a><span class="lineno">  216</span> </div>
<div class="foldopen" id="foldopen00217" data-start="{" data-end="}">
<div class="line"><a id="l00217" name="l00217"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_mapped_tensor.html#abf8ddc60a224bc676dd0fad40ad2f43b">  217</a></span>    <a class="code hl_class" href="classnz_1_1data_1_1_dimension.html">MappedTensor::shape_type</a> <a class="code hl_function" href="classnz_1_1data_1_1_mapped_tensor.html#abf8ddc60a224bc676dd0fad40ad2f43b">MappedTensor::shape</a>() const noexcept {</div>
<div class="line"><a id="l00218" name="l00218"></a><span class="lineno">  218</span>        <span class="keywordflow">return</span> _shape;</div>
<div class="line"><a id="l00219" name="l00219"></a><span class="lineno">  219</span>    }</div>
</div>
<div class="line"><a id="l00220" name="l00220"></a><span class="lineno">  220</span> </div>
<div class="foldopen" id="foldopen00221" data-start="{" data-end="}">
<div class="line"><a id="l00221" name="l00221"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_mapped_tensor.html#add503096ece511cc5b44f75271ff424d">  221</a></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classnz_1_1data_1_1_mapped_tensor.html#add503096ece511cc5b44f75271ff424d">MappedTensor::setRequiresGrad</a>(<span class="keyword">const</span> <span class="keywordtype">bool</span> requires_grad) {</div>
<div class="line"><a id="l00222" name="l00222"></a><span class="lineno">  222</span>        <span class="keywordflow">if</span> (_requires_grad &amp;&amp; !requires_grad) {</div>
<div class="line"><a id="l00223" name="l00223"></a><span class="lineno">  223</span>            <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab6803232b9c08d9282b16322a6c7b8a9">freeHost</a>(_grad);</div>
<div class="line"><a id="l00224" name="l00224"></a><span class="lineno">  224</span>            _grad = <span class="keyword">nullptr</span>;</div>
<div class="line"><a id="l00225" name="l00225"></a><span class="lineno">  225</span>            _requires_grad = requires_grad;</div>
<div class="line"><a id="l00226" name="l00226"></a><span class="lineno">  226</span>        }</div>
<div class="line"><a id="l00227" name="l00227"></a><span class="lineno">  227</span>        <span class="keywordflow">if</span> (!_requires_grad &amp;&amp; requires_grad) {</div>
<div class="line"><a id="l00228" name="l00228"></a><span class="lineno">  228</span>            CHECK(cudaMallocHost(&amp;_grad, _size * <span class="keyword">sizeof</span>(value_type)));</div>
<div class="line"><a id="l00229" name="l00229"></a><span class="lineno">  229</span>            _requires_grad = requires_grad;</div>
<div class="line"><a id="l00230" name="l00230"></a><span class="lineno">  230</span>        }</div>
<div class="line"><a id="l00231" name="l00231"></a><span class="lineno">  231</span>    }</div>
</div>
<div class="line"><a id="l00232" name="l00232"></a><span class="lineno">  232</span> </div>
<div class="foldopen" id="foldopen00233" data-start="{" data-end="}">
<div class="line"><a id="l00233" name="l00233"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_mapped_tensor.html#aefb5c705fc6d224469978cf64ca37e0b">  233</a></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classnz_1_1data_1_1_mapped_tensor.html#aefb5c705fc6d224469978cf64ca37e0b">MappedTensor::setShape</a>(<span class="keyword">const</span> <a class="code hl_class" href="classnz_1_1data_1_1_dimension.html">shape_type</a>&amp; shape) {</div>
<div class="line"><a id="l00234" name="l00234"></a><span class="lineno">  234</span>        _shape.<a class="code hl_function" href="classnz_1_1data_1_1_dimension.html#accb260af17b2e888268e1a7d3cdccc71">reshape</a>(<a class="code hl_function" href="classnz_1_1data_1_1_mapped_tensor.html#abf8ddc60a224bc676dd0fad40ad2f43b">shape</a>);</div>
<div class="line"><a id="l00235" name="l00235"></a><span class="lineno">  235</span>    }</div>
</div>
<div class="line"><a id="l00236" name="l00236"></a><span class="lineno">  236</span> </div>
<div class="foldopen" id="foldopen00237" data-start="{" data-end="}">
<div class="line"><a id="l00237" name="l00237"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_mapped_tensor.html#a34906f06e918a40089db7a8936ee1608">  237</a></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classnz_1_1data_1_1_mapped_tensor.html#a34906f06e918a40089db7a8936ee1608">MappedTensor::dataInject</a>(<span class="keywordtype">float</span>* data, <span class="keyword">const</span> size_type size, <span class="keyword">const</span> <span class="keywordtype">bool</span> isGrad)<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00238" name="l00238"></a><span class="lineno">  238</span>        <span class="keywordflow">if</span> (isGrad) {</div>
<div class="line"><a id="l00239" name="l00239"></a><span class="lineno">  239</span>            <span class="keywordflow">if</span> (!_requires_grad) {</div>
<div class="line"><a id="l00240" name="l00240"></a><span class="lineno">  240</span>                <span class="keywordflow">throw</span> std::invalid_argument(</div>
<div class="line"><a id="l00241" name="l00241"></a><span class="lineno">  241</span>                    <span class="stringliteral">&quot;Gradient injection is not allowed for tensors that do not require gradients.&quot;</span>);</div>
<div class="line"><a id="l00242" name="l00242"></a><span class="lineno">  242</span>            }</div>
<div class="line"><a id="l00243" name="l00243"></a><span class="lineno">  243</span>            <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#afa38d5c6db0e6b48c8f74ce8ad0df2bc">memcpy</a>(_grad, <a class="code hl_function" href="classnz_1_1data_1_1_mapped_tensor.html#ad72562ee8cad3196dc9ffbdd436f74a3">data</a>,</div>
<div class="line"><a id="l00244" name="l00244"></a><span class="lineno">  244</span>                                                                 (<a class="code hl_function" href="classnz_1_1data_1_1_mapped_tensor.html#aa144f9f3b76741e5d3fb3b5df3cc3b42">size</a> &lt; _size ? <a class="code hl_function" href="classnz_1_1data_1_1_mapped_tensor.html#aa144f9f3b76741e5d3fb3b5df3cc3b42">size</a> : _size) * <span class="keyword">sizeof</span>(value_type),</div>
<div class="line"><a id="l00245" name="l00245"></a><span class="lineno">  245</span>                                                                 cudaMemcpyHostToDevice);</div>
<div class="line"><a id="l00246" name="l00246"></a><span class="lineno">  246</span>        }</div>
<div class="line"><a id="l00247" name="l00247"></a><span class="lineno">  247</span>        <span class="keywordflow">else</span> {</div>
<div class="line"><a id="l00248" name="l00248"></a><span class="lineno">  248</span>            <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#afa38d5c6db0e6b48c8f74ce8ad0df2bc">memcpy</a>(_data, <a class="code hl_function" href="classnz_1_1data_1_1_mapped_tensor.html#ad72562ee8cad3196dc9ffbdd436f74a3">data</a>,</div>
<div class="line"><a id="l00249" name="l00249"></a><span class="lineno">  249</span>                                                                 (<a class="code hl_function" href="classnz_1_1data_1_1_mapped_tensor.html#aa144f9f3b76741e5d3fb3b5df3cc3b42">size</a> &lt; _size ? <a class="code hl_function" href="classnz_1_1data_1_1_mapped_tensor.html#aa144f9f3b76741e5d3fb3b5df3cc3b42">size</a> : _size) * <span class="keyword">sizeof</span>(value_type),</div>
<div class="line"><a id="l00250" name="l00250"></a><span class="lineno">  250</span>                                                                 cudaMemcpyHostToDevice);</div>
<div class="line"><a id="l00251" name="l00251"></a><span class="lineno">  251</span>        }</div>
<div class="line"><a id="l00252" name="l00252"></a><span class="lineno">  252</span>    }</div>
</div>
<div class="line"><a id="l00253" name="l00253"></a><span class="lineno">  253</span> </div>
<div class="foldopen" id="foldopen00254" data-start="{" data-end="}">
<div class="line"><a id="l00254" name="l00254"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_mapped_tensor.html#adbbf1cda4d9806c93ad30316ce507630">  254</a></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classnz_1_1data_1_1_mapped_tensor.html#a34906f06e918a40089db7a8936ee1608">MappedTensor::dataInject</a>(<span class="keyword">const</span> std::initializer_list&lt;value_type&gt;&amp; data, <span class="keyword">const</span> <span class="keywordtype">bool</span> isGrad)<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00255" name="l00255"></a><span class="lineno">  255</span>        <span class="keywordflow">if</span> (isGrad &amp;&amp; !_requires_grad) {</div>
<div class="line"><a id="l00256" name="l00256"></a><span class="lineno">  256</span>            <span class="keywordflow">throw</span> std::invalid_argument(<span class="stringliteral">&quot;Gradient injection is not allowed for tensors that do not require gradients.&quot;</span>);</div>
<div class="line"><a id="l00257" name="l00257"></a><span class="lineno">  257</span>        }</div>
<div class="line"><a id="l00258" name="l00258"></a><span class="lineno">  258</span>        <span class="keywordflow">for</span> (size_type i = 0; i &lt; (<a class="code hl_function" href="classnz_1_1data_1_1_mapped_tensor.html#ad72562ee8cad3196dc9ffbdd436f74a3">data</a>.size() &lt; _size ? <a class="code hl_function" href="classnz_1_1data_1_1_mapped_tensor.html#ad72562ee8cad3196dc9ffbdd436f74a3">data</a>.size() : _size); i++) {</div>
<div class="line"><a id="l00259" name="l00259"></a><span class="lineno">  259</span>            <span class="keywordflow">if</span> (isGrad) {</div>
<div class="line"><a id="l00260" name="l00260"></a><span class="lineno">  260</span>                _grad[i] = *(<a class="code hl_function" href="classnz_1_1data_1_1_mapped_tensor.html#ad72562ee8cad3196dc9ffbdd436f74a3">data</a>.begin() + i);</div>
<div class="line"><a id="l00261" name="l00261"></a><span class="lineno">  261</span>            }</div>
<div class="line"><a id="l00262" name="l00262"></a><span class="lineno">  262</span>            <span class="keywordflow">else</span> {</div>
<div class="line"><a id="l00263" name="l00263"></a><span class="lineno">  263</span>                _data[i] = *(<a class="code hl_function" href="classnz_1_1data_1_1_mapped_tensor.html#ad72562ee8cad3196dc9ffbdd436f74a3">data</a>.begin() + i);</div>
<div class="line"><a id="l00264" name="l00264"></a><span class="lineno">  264</span>            }</div>
<div class="line"><a id="l00265" name="l00265"></a><span class="lineno">  265</span>        }</div>
<div class="line"><a id="l00266" name="l00266"></a><span class="lineno">  266</span>    }</div>
</div>
<div class="line"><a id="l00267" name="l00267"></a><span class="lineno">  267</span> </div>
<div class="foldopen" id="foldopen00268" data-start="{" data-end="}">
<div class="line"><a id="l00268" name="l00268"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_mapped_tensor.html#a8a42265d7c9f964c41ff43e9d8dc7bf7">  268</a></span>    std::ostream&amp; <a class="code hl_function" href="classnz_1_1data_1_1_mapped_tensor.html#a8a42265d7c9f964c41ff43e9d8dc7bf7">MappedTensor::print</a>(std::ostream&amp; os)<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00269" name="l00269"></a><span class="lineno">  269</span>        <span class="keyword">const</span> std::ostream_iterator&lt;value_type&gt; oit(os, <span class="stringliteral">&quot; &quot;</span>);</div>
<div class="line"><a id="l00270" name="l00270"></a><span class="lineno">  270</span>        <a class="code hl_function" href="classnz_1_1data_1_1_mapped_tensor.html#ae133d08d4505a30c6bd34e4a7a311f9e">syncData</a>();</div>
<div class="line"><a id="l00271" name="l00271"></a><span class="lineno">  271</span>        <span class="keyword">const</span> <span class="keyword">auto</span>&amp; n = _shape.<a class="code hl_function" href="classnz_1_1data_1_1_dimension.html#acc472e84b4c44f649f34b6fbb0eeacf7">N</a>();</div>
<div class="line"><a id="l00272" name="l00272"></a><span class="lineno">  272</span>        <span class="keyword">const</span> <span class="keyword">auto</span>&amp; c = _shape.<a class="code hl_function" href="classnz_1_1data_1_1_dimension.html#ae1e87c4a462dd60e02821aa27ffc7e09">C</a>();</div>
<div class="line"><a id="l00273" name="l00273"></a><span class="lineno">  273</span>        <span class="keyword">const</span> <span class="keyword">auto</span>&amp; h = _shape.<a class="code hl_function" href="classnz_1_1data_1_1_dimension.html#a7eb3acc882c48e775c418d97f709240f">H</a>();</div>
<div class="line"><a id="l00274" name="l00274"></a><span class="lineno">  274</span>        <span class="keyword">const</span> <span class="keyword">auto</span>&amp; w = _shape.<a class="code hl_function" href="classnz_1_1data_1_1_dimension.html#a65773c675476dfea3f06b30f21ebbedd">W</a>();</div>
<div class="line"><a id="l00275" name="l00275"></a><span class="lineno">  275</span> </div>
<div class="line"><a id="l00276" name="l00276"></a><span class="lineno">  276</span>        <span class="keywordflow">for</span> (<span class="keyword">auto</span> ni = 0; ni &lt; n; ++ni) {</div>
<div class="line"><a id="l00277" name="l00277"></a><span class="lineno">  277</span>            os &lt;&lt; <span class="stringliteral">&quot;n=&quot;</span> &lt;&lt; ni &lt;&lt; <span class="stringliteral">&quot; [\n&quot;</span>;</div>
<div class="line"><a id="l00278" name="l00278"></a><span class="lineno">  278</span>            <span class="keywordflow">for</span> (<span class="keyword">auto</span> ci = 0; ci &lt; c; ++ci) {</div>
<div class="line"><a id="l00279" name="l00279"></a><span class="lineno">  279</span>                os &lt;&lt; <span class="stringliteral">&quot;  c=&quot;</span> &lt;&lt; ci &lt;&lt; <span class="stringliteral">&quot; [\n&quot;</span>;</div>
<div class="line"><a id="l00280" name="l00280"></a><span class="lineno">  280</span>                <span class="keywordflow">for</span> (<span class="keyword">auto</span> hi = 0; hi &lt; h; ++hi) {</div>
<div class="line"><a id="l00281" name="l00281"></a><span class="lineno">  281</span>                    <span class="keyword">const</span> <span class="keyword">auto</span> offset = ni * _shape.<a class="code hl_function" href="classnz_1_1data_1_1_dimension.html#a4831fea5aaf7dbad3578d3fa8e55aef1">getStride</a>(0) + ci * _shape.<a class="code hl_function" href="classnz_1_1data_1_1_dimension.html#a4831fea5aaf7dbad3578d3fa8e55aef1">getStride</a>(1) + hi * _shape.<a class="code hl_function" href="classnz_1_1data_1_1_dimension.html#a4831fea5aaf7dbad3578d3fa8e55aef1">getStride</a>(2);</div>
<div class="line"><a id="l00282" name="l00282"></a><span class="lineno">  282</span>                    <span class="keyword">const</span> <span class="keyword">auto</span>* <a class="code hl_function" href="classnz_1_1data_1_1_mapped_tensor.html#ad76dcba2cd22c53b8c4815da113dcb56">begin</a> = _data + offset;</div>
<div class="line"><a id="l00283" name="l00283"></a><span class="lineno">  283</span>                    <span class="keyword">const</span> <span class="keyword">auto</span>* <a class="code hl_function" href="classnz_1_1data_1_1_mapped_tensor.html#ac01f432f0ef9b7279630b5b35bc609e0">end</a> = <a class="code hl_function" href="classnz_1_1data_1_1_mapped_tensor.html#ad76dcba2cd22c53b8c4815da113dcb56">begin</a> + w;</div>
<div class="line"><a id="l00284" name="l00284"></a><span class="lineno">  284</span>                    os &lt;&lt; <span class="stringliteral">&quot;    [&quot;</span>;</div>
<div class="line"><a id="l00285" name="l00285"></a><span class="lineno">  285</span>                    std::copy(<a class="code hl_function" href="classnz_1_1data_1_1_mapped_tensor.html#ad76dcba2cd22c53b8c4815da113dcb56">begin</a>, <a class="code hl_function" href="classnz_1_1data_1_1_mapped_tensor.html#ac01f432f0ef9b7279630b5b35bc609e0">end</a>, oit);</div>
<div class="line"><a id="l00286" name="l00286"></a><span class="lineno">  286</span>                    os &lt;&lt; <span class="stringliteral">&quot;]\n&quot;</span>;</div>
<div class="line"><a id="l00287" name="l00287"></a><span class="lineno">  287</span>                }</div>
<div class="line"><a id="l00288" name="l00288"></a><span class="lineno">  288</span>                os &lt;&lt; <span class="stringliteral">&quot;  ]\n&quot;</span>;</div>
<div class="line"><a id="l00289" name="l00289"></a><span class="lineno">  289</span>            }</div>
<div class="line"><a id="l00290" name="l00290"></a><span class="lineno">  290</span>            os &lt;&lt; <span class="stringliteral">&quot;]\n\n&quot;</span>;</div>
<div class="line"><a id="l00291" name="l00291"></a><span class="lineno">  291</span>        }</div>
<div class="line"><a id="l00292" name="l00292"></a><span class="lineno">  292</span>        <span class="keywordflow">return</span> os;</div>
<div class="line"><a id="l00293" name="l00293"></a><span class="lineno">  293</span>    }</div>
</div>
<div class="line"><a id="l00294" name="l00294"></a><span class="lineno">  294</span> </div>
<div class="line"><a id="l00295" name="l00295"></a><span class="lineno">  295</span> </div>
<div class="foldopen" id="foldopen00296" data-start="{" data-end="}">
<div class="line"><a id="l00296" name="l00296"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_mapped_tensor.html#ab54e80ac0d2ec90442b0a674004cfdbc">  296</a></span>    std::ostream&amp; <a class="code hl_function" href="classnz_1_1data_1_1_mapped_tensor.html#ab54e80ac0d2ec90442b0a674004cfdbc">MappedTensor::printGrad</a>(std::ostream&amp; os)<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00297" name="l00297"></a><span class="lineno">  297</span>        <span class="keywordflow">if</span> (!_requires_grad) {</div>
<div class="line"><a id="l00298" name="l00298"></a><span class="lineno">  298</span>            <span class="keywordflow">throw</span> std::invalid_argument(<span class="stringliteral">&quot;Gradient printing is not allowed for tensors that do not require gradients.&quot;</span>);</div>
<div class="line"><a id="l00299" name="l00299"></a><span class="lineno">  299</span>        }</div>
<div class="line"><a id="l00300" name="l00300"></a><span class="lineno">  300</span>        <span class="keyword">const</span> std::ostream_iterator&lt;value_type&gt; oit(os, <span class="stringliteral">&quot; &quot;</span>);</div>
<div class="line"><a id="l00301" name="l00301"></a><span class="lineno">  301</span>        <a class="code hl_function" href="classnz_1_1data_1_1_mapped_tensor.html#af9d1b0fa71f9bb7686cc26ae7b32be8d">syncGrad</a>();</div>
<div class="line"><a id="l00302" name="l00302"></a><span class="lineno">  302</span>        <span class="keyword">const</span> <span class="keyword">auto</span>&amp; n = _shape.<a class="code hl_function" href="classnz_1_1data_1_1_dimension.html#acc472e84b4c44f649f34b6fbb0eeacf7">N</a>();</div>
<div class="line"><a id="l00303" name="l00303"></a><span class="lineno">  303</span>        <span class="keyword">const</span> <span class="keyword">auto</span>&amp; c = _shape.<a class="code hl_function" href="classnz_1_1data_1_1_dimension.html#ae1e87c4a462dd60e02821aa27ffc7e09">C</a>();</div>
<div class="line"><a id="l00304" name="l00304"></a><span class="lineno">  304</span>        <span class="keyword">const</span> <span class="keyword">auto</span>&amp; h = _shape.<a class="code hl_function" href="classnz_1_1data_1_1_dimension.html#a7eb3acc882c48e775c418d97f709240f">H</a>();</div>
<div class="line"><a id="l00305" name="l00305"></a><span class="lineno">  305</span>        <span class="keyword">const</span> <span class="keyword">auto</span>&amp; w = _shape.<a class="code hl_function" href="classnz_1_1data_1_1_dimension.html#a65773c675476dfea3f06b30f21ebbedd">W</a>();</div>
<div class="line"><a id="l00306" name="l00306"></a><span class="lineno">  306</span> </div>
<div class="line"><a id="l00307" name="l00307"></a><span class="lineno">  307</span>        <span class="keywordflow">for</span> (<span class="keyword">auto</span> ni = 0; ni &lt; n; ++ni) {</div>
<div class="line"><a id="l00308" name="l00308"></a><span class="lineno">  308</span>            os &lt;&lt; <span class="stringliteral">&quot;n=&quot;</span> &lt;&lt; ni &lt;&lt; <span class="stringliteral">&quot; [\n&quot;</span>;</div>
<div class="line"><a id="l00309" name="l00309"></a><span class="lineno">  309</span>            <span class="keywordflow">for</span> (<span class="keyword">auto</span> ci = 0; ci &lt; c; ++ci) {</div>
<div class="line"><a id="l00310" name="l00310"></a><span class="lineno">  310</span>                os &lt;&lt; <span class="stringliteral">&quot;  c=&quot;</span> &lt;&lt; ci &lt;&lt; <span class="stringliteral">&quot; [\n&quot;</span>;</div>
<div class="line"><a id="l00311" name="l00311"></a><span class="lineno">  311</span>                <span class="keywordflow">for</span> (<span class="keyword">auto</span> hi = 0; hi &lt; h; ++hi) {</div>
<div class="line"><a id="l00312" name="l00312"></a><span class="lineno">  312</span>                    <span class="keyword">const</span> <span class="keyword">auto</span> offset = ni * _shape.<a class="code hl_function" href="classnz_1_1data_1_1_dimension.html#a4831fea5aaf7dbad3578d3fa8e55aef1">getStride</a>(0) + ci * _shape.<a class="code hl_function" href="classnz_1_1data_1_1_dimension.html#a4831fea5aaf7dbad3578d3fa8e55aef1">getStride</a>(1) + hi * _shape.<a class="code hl_function" href="classnz_1_1data_1_1_dimension.html#a4831fea5aaf7dbad3578d3fa8e55aef1">getStride</a>(2);</div>
<div class="line"><a id="l00313" name="l00313"></a><span class="lineno">  313</span>                    <span class="keyword">const</span> <span class="keyword">auto</span>* <a class="code hl_function" href="classnz_1_1data_1_1_mapped_tensor.html#ad76dcba2cd22c53b8c4815da113dcb56">begin</a> = _grad + offset;</div>
<div class="line"><a id="l00314" name="l00314"></a><span class="lineno">  314</span>                    <span class="keyword">const</span> <span class="keyword">auto</span>* <a class="code hl_function" href="classnz_1_1data_1_1_mapped_tensor.html#ac01f432f0ef9b7279630b5b35bc609e0">end</a> = <a class="code hl_function" href="classnz_1_1data_1_1_mapped_tensor.html#ad76dcba2cd22c53b8c4815da113dcb56">begin</a> + w;</div>
<div class="line"><a id="l00315" name="l00315"></a><span class="lineno">  315</span>                    os &lt;&lt; <span class="stringliteral">&quot;    [&quot;</span>;</div>
<div class="line"><a id="l00316" name="l00316"></a><span class="lineno">  316</span>                    std::copy(<a class="code hl_function" href="classnz_1_1data_1_1_mapped_tensor.html#ad76dcba2cd22c53b8c4815da113dcb56">begin</a>, <a class="code hl_function" href="classnz_1_1data_1_1_mapped_tensor.html#ac01f432f0ef9b7279630b5b35bc609e0">end</a>, oit);</div>
<div class="line"><a id="l00317" name="l00317"></a><span class="lineno">  317</span>                    os &lt;&lt; <span class="stringliteral">&quot;]\n&quot;</span>;</div>
<div class="line"><a id="l00318" name="l00318"></a><span class="lineno">  318</span>                }</div>
<div class="line"><a id="l00319" name="l00319"></a><span class="lineno">  319</span>                os &lt;&lt; <span class="stringliteral">&quot;  ]\n&quot;</span>;</div>
<div class="line"><a id="l00320" name="l00320"></a><span class="lineno">  320</span>            }</div>
<div class="line"><a id="l00321" name="l00321"></a><span class="lineno">  321</span>            os &lt;&lt; <span class="stringliteral">&quot;]\n\n&quot;</span>;</div>
<div class="line"><a id="l00322" name="l00322"></a><span class="lineno">  322</span>        }</div>
<div class="line"><a id="l00323" name="l00323"></a><span class="lineno">  323</span>        <span class="keywordflow">return</span> os;</div>
<div class="line"><a id="l00324" name="l00324"></a><span class="lineno">  324</span>    }</div>
</div>
<div class="line"><a id="l00325" name="l00325"></a><span class="lineno">  325</span> </div>
<div class="foldopen" id="foldopen00326" data-start="{" data-end="}">
<div class="line"><a id="l00326" name="l00326"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_mapped_tensor.html#abb30df1c0174d5a8d9fa8911f7b3d3bc">  326</a></span>    <span class="keyword">auto</span> <a class="code hl_function" href="classnz_1_1data_1_1_mapped_tensor.html#abb30df1c0174d5a8d9fa8911f7b3d3bc">MappedTensor::operator[]</a>(<span class="keyword">const</span> size_type index) <span class="keyword">const</span> -&gt; value_type&amp; {</div>
<div class="line"><a id="l00327" name="l00327"></a><span class="lineno">  327</span>        syncData();</div>
<div class="line"><a id="l00328" name="l00328"></a><span class="lineno">  328</span>        <span class="keywordflow">if</span> (index &gt;= _size) {</div>
<div class="line"><a id="l00329" name="l00329"></a><span class="lineno">  329</span>            <span class="keywordflow">throw</span> std::out_of_range(<span class="stringliteral">&quot;Index out of range.&quot;</span>);</div>
<div class="line"><a id="l00330" name="l00330"></a><span class="lineno">  330</span>        }</div>
<div class="line"><a id="l00331" name="l00331"></a><span class="lineno">  331</span>        <span class="keywordflow">return</span> _data[index];</div>
<div class="line"><a id="l00332" name="l00332"></a><span class="lineno">  332</span>    }</div>
</div>
<div class="line"><a id="l00333" name="l00333"></a><span class="lineno">  333</span> </div>
<div class="foldopen" id="foldopen00334" data-start="{" data-end="}">
<div class="line"><a id="l00334" name="l00334"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_mapped_tensor.html#a0b348f12004c0b5eeb46f8f1d4b0bf53">  334</a></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classnz_1_1data_1_1_mapped_tensor.html#a0b348f12004c0b5eeb46f8f1d4b0bf53">MappedTensor::clear</a>()<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00335" name="l00335"></a><span class="lineno">  335</span>        <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#a71ad766cb2869d3dd6a3931966e81706">memset</a>(_data, 0, _size * <span class="keyword">sizeof</span>(value_type));</div>
<div class="line"><a id="l00336" name="l00336"></a><span class="lineno">  336</span>    }</div>
</div>
<div class="line"><a id="l00337" name="l00337"></a><span class="lineno">  337</span> </div>
<div class="foldopen" id="foldopen00338" data-start="{" data-end="}">
<div class="line"><a id="l00338" name="l00338"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_mapped_tensor.html#a8ced8f419ebc96c0a1b58e7d271aaa0a">  338</a></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classnz_1_1data_1_1_mapped_tensor.html#a8ced8f419ebc96c0a1b58e7d271aaa0a">MappedTensor::clearGrad</a>()<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00339" name="l00339"></a><span class="lineno">  339</span>        <span class="keywordflow">if</span> (_requires_grad) {</div>
<div class="line"><a id="l00340" name="l00340"></a><span class="lineno">  340</span>            <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#a71ad766cb2869d3dd6a3931966e81706">memset</a>(_grad, 0, _size * <span class="keyword">sizeof</span>(value_type));</div>
<div class="line"><a id="l00341" name="l00341"></a><span class="lineno">  341</span>        }</div>
<div class="line"><a id="l00342" name="l00342"></a><span class="lineno">  342</span>        <span class="keywordflow">else</span> {</div>
<div class="line"><a id="l00343" name="l00343"></a><span class="lineno">  343</span>            <span class="keywordflow">throw</span> std::runtime_error(<span class="stringliteral">&quot;Gradient clearing is not allowed for tensors that do not require gradients.&quot;</span>);</div>
<div class="line"><a id="l00344" name="l00344"></a><span class="lineno">  344</span>        }</div>
<div class="line"><a id="l00345" name="l00345"></a><span class="lineno">  345</span>    }</div>
</div>
<div class="line"><a id="l00346" name="l00346"></a><span class="lineno">  346</span> </div>
<div class="foldopen" id="foldopen00347" data-start="{" data-end="}">
<div class="line"><a id="l00347" name="l00347"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_mapped_tensor.html#a54d785fecf6466bd442989545ead6de4">  347</a></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classnz_1_1data_1_1_mapped_tensor.html#a54d785fecf6466bd442989545ead6de4">MappedTensor::reshape</a>(<span class="keyword">const</span> <a class="code hl_class" href="classnz_1_1data_1_1_dimension.html">shape_type</a>&amp; shape) {</div>
<div class="line"><a id="l00348" name="l00348"></a><span class="lineno">  348</span>        <span class="keyword">const</span> size_type newSize = <a class="code hl_function" href="classnz_1_1data_1_1_mapped_tensor.html#abf8ddc60a224bc676dd0fad40ad2f43b">shape</a>.<a class="code hl_function" href="classnz_1_1data_1_1_dimension.html#a073622bb031999163987ccf77f8edfb2">size</a>();</div>
<div class="line"><a id="l00349" name="l00349"></a><span class="lineno">  349</span>        <span class="keywordflow">if</span> (newSize != _size) {</div>
<div class="line"><a id="l00350" name="l00350"></a><span class="lineno">  350</span>            WARN(<span class="stringliteral">&quot;Reshaping to a different size will cause data loss&quot;</span>);</div>
<div class="line"><a id="l00351" name="l00351"></a><span class="lineno">  351</span>        }</div>
<div class="line"><a id="l00352" name="l00352"></a><span class="lineno">  352</span>        value_type* temp;</div>
<div class="line"><a id="l00353" name="l00353"></a><span class="lineno">  353</span>        CHECK(cudaMallocHost(&amp;temp, newSize * <span class="keyword">sizeof</span>(value_type)));</div>
<div class="line"><a id="l00354" name="l00354"></a><span class="lineno">  354</span>        <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#a71ad766cb2869d3dd6a3931966e81706">memset</a>(temp, 0, newSize * <span class="keyword">sizeof</span>(value_type));</div>
<div class="line"><a id="l00355" name="l00355"></a><span class="lineno">  355</span>        <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#afa38d5c6db0e6b48c8f74ce8ad0df2bc">memcpy</a>(temp, _data,</div>
<div class="line"><a id="l00356" name="l00356"></a><span class="lineno">  356</span>                                                             (_size &lt; newSize ? _size : newSize) * <span class="keyword">sizeof</span>(value_type),</div>
<div class="line"><a id="l00357" name="l00357"></a><span class="lineno">  357</span>                                                             cudaMemcpyDeviceToDevice);</div>
<div class="line"><a id="l00358" name="l00358"></a><span class="lineno">  358</span>        <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab6803232b9c08d9282b16322a6c7b8a9">freeHost</a>(_data);</div>
<div class="line"><a id="l00359" name="l00359"></a><span class="lineno">  359</span>        _data = temp;</div>
<div class="line"><a id="l00360" name="l00360"></a><span class="lineno">  360</span>        <span class="keywordflow">if</span> (_requires_grad) {</div>
<div class="line"><a id="l00361" name="l00361"></a><span class="lineno">  361</span>            value_type* tempGrad;</div>
<div class="line"><a id="l00362" name="l00362"></a><span class="lineno">  362</span>            CHECK(cudaMallocHost(&amp;tempGrad, newSize * <span class="keyword">sizeof</span>(value_type)));</div>
<div class="line"><a id="l00363" name="l00363"></a><span class="lineno">  363</span>            <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#a71ad766cb2869d3dd6a3931966e81706">memset</a>(tempGrad, 0, newSize * <span class="keyword">sizeof</span>(value_type));</div>
<div class="line"><a id="l00364" name="l00364"></a><span class="lineno">  364</span>            <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#afa38d5c6db0e6b48c8f74ce8ad0df2bc">memcpy</a>(tempGrad, _grad,</div>
<div class="line"><a id="l00365" name="l00365"></a><span class="lineno">  365</span>                                                                 (_size &lt; newSize ? _size : newSize) * <span class="keyword">sizeof</span>(</div>
<div class="line"><a id="l00366" name="l00366"></a><span class="lineno">  366</span>                                                                     value_type), cudaMemcpyDeviceToDevice);</div>
<div class="line"><a id="l00367" name="l00367"></a><span class="lineno">  367</span>            <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab6803232b9c08d9282b16322a6c7b8a9">freeHost</a>(_grad);</div>
<div class="line"><a id="l00368" name="l00368"></a><span class="lineno">  368</span>            _grad = tempGrad;</div>
<div class="line"><a id="l00369" name="l00369"></a><span class="lineno">  369</span>        }</div>
<div class="line"><a id="l00370" name="l00370"></a><span class="lineno">  370</span>        _shape = <a class="code hl_function" href="classnz_1_1data_1_1_mapped_tensor.html#abf8ddc60a224bc676dd0fad40ad2f43b">shape</a>;</div>
<div class="line"><a id="l00371" name="l00371"></a><span class="lineno">  371</span>        _size = newSize;</div>
<div class="line"><a id="l00372" name="l00372"></a><span class="lineno">  372</span>    }</div>
</div>
<div class="line"><a id="l00373" name="l00373"></a><span class="lineno">  373</span> </div>
<div class="foldopen" id="foldopen00374" data-start="{" data-end="}">
<div class="line"><a id="l00374" name="l00374"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_mapped_tensor.html#a0150ae0b7137a617c598d1f71d69ce1a">  374</a></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classnz_1_1data_1_1_mapped_tensor.html#a0150ae0b7137a617c598d1f71d69ce1a">MappedTensor::randomize</a>(size_type seed, <span class="keyword">const</span> <span class="keywordtype">bool</span> isGrad)<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00375" name="l00375"></a><span class="lineno">  375</span>        <span class="keywordflow">if</span> (isGrad &amp;&amp; !_requires_grad) {</div>
<div class="line"><a id="l00376" name="l00376"></a><span class="lineno">  376</span>            <span class="keywordflow">throw</span> std::invalid_argument(</div>
<div class="line"><a id="l00377" name="l00377"></a><span class="lineno">  377</span>                <span class="stringliteral">&quot;Gradient randomization is not allowed for tensors that do not require gradients.&quot;</span>);</div>
<div class="line"><a id="l00378" name="l00378"></a><span class="lineno">  378</span>        }</div>
<div class="line"><a id="l00379" name="l00379"></a><span class="lineno">  379</span>        <span class="keywordflow">if</span> (!seed) {</div>
<div class="line"><a id="l00380" name="l00380"></a><span class="lineno">  380</span>            seed = std::chrono::system_clock::now().time_since_epoch().count();</div>
<div class="line"><a id="l00381" name="l00381"></a><span class="lineno">  381</span>        }</div>
<div class="line"><a id="l00382" name="l00382"></a><span class="lineno">  382</span>        curandGenerator_t generator;</div>
<div class="line"><a id="l00383" name="l00383"></a><span class="lineno">  383</span>        curandStatus_t status = curandCreateGenerator(&amp;generator, CURAND_RNG_PSEUDO_DEFAULT);</div>
<div class="line"><a id="l00384" name="l00384"></a><span class="lineno">  384</span>        <span class="keywordflow">if</span> (status != CURAND_STATUS_SUCCESS) {</div>
<div class="line"><a id="l00385" name="l00385"></a><span class="lineno">  385</span>            <span class="keywordflow">throw</span> std::runtime_error(<span class="stringliteral">&quot;Failed to create CURAND generator.&quot;</span>);</div>
<div class="line"><a id="l00386" name="l00386"></a><span class="lineno">  386</span>        }</div>
<div class="line"><a id="l00387" name="l00387"></a><span class="lineno">  387</span>        status = curandSetPseudoRandomGeneratorSeed(generator, seed);</div>
<div class="line"><a id="l00388" name="l00388"></a><span class="lineno">  388</span>        <span class="keywordflow">if</span> (status != CURAND_STATUS_SUCCESS) {</div>
<div class="line"><a id="l00389" name="l00389"></a><span class="lineno">  389</span>            <span class="keywordflow">throw</span> std::runtime_error(<span class="stringliteral">&quot;Failed to set CURAND seed.&quot;</span>);</div>
<div class="line"><a id="l00390" name="l00390"></a><span class="lineno">  390</span>        }</div>
<div class="line"><a id="l00391" name="l00391"></a><span class="lineno">  391</span>        status = curandGenerateUniform(generator, isGrad ? _grad : _data, _size);</div>
<div class="line"><a id="l00392" name="l00392"></a><span class="lineno">  392</span>        <span class="keywordflow">if</span> (status != CURAND_STATUS_SUCCESS) {</div>
<div class="line"><a id="l00393" name="l00393"></a><span class="lineno">  393</span>            <span class="keywordflow">throw</span> std::runtime_error(<span class="stringliteral">&quot;Failed to generate random numbers.&quot;</span>);</div>
<div class="line"><a id="l00394" name="l00394"></a><span class="lineno">  394</span>        }</div>
<div class="line"><a id="l00395" name="l00395"></a><span class="lineno">  395</span>    }</div>
</div>
<div class="line"><a id="l00396" name="l00396"></a><span class="lineno">  396</span> </div>
<div class="foldopen" id="foldopen00397" data-start="{" data-end="}">
<div class="line"><a id="l00397" name="l00397"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_mapped_tensor.html#acf793cf0d23af29076d18cd46a6d19cf">  397</a></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classnz_1_1data_1_1_mapped_tensor.html#acf793cf0d23af29076d18cd46a6d19cf">MappedTensor::fill</a>(<span class="keyword">const</span> value_type value, <span class="keyword">const</span> <span class="keywordtype">bool</span> isGrad)<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00398" name="l00398"></a><span class="lineno">  398</span>        <span class="keywordflow">if</span> (isGrad &amp;&amp; !_requires_grad) {</div>
<div class="line"><a id="l00399" name="l00399"></a><span class="lineno">  399</span>            <span class="keywordflow">throw</span> std::invalid_argument(</div>
<div class="line"><a id="l00400" name="l00400"></a><span class="lineno">  400</span>                <span class="stringliteral">&quot;Gradient filling is not allowed for tensors that do not require gradients.&quot;</span>);</div>
<div class="line"><a id="l00401" name="l00401"></a><span class="lineno">  401</span>        }</div>
<div class="line"><a id="l00402" name="l00402"></a><span class="lineno">  402</span>        <span class="keyword">const</span> dim3 block(512);</div>
<div class="line"><a id="l00403" name="l00403"></a><span class="lineno">  403</span>        <span class="keyword">const</span> dim3 grid((_size + block.x - 1) / block.x);</div>
<div class="line"><a id="l00404" name="l00404"></a><span class="lineno">  404</span>        <a class="code hl_function" href="namespacenz_1_1krnl.html#ad136c8a6560a5305984ce0a31bea71bf">krnl::Fill</a>(grid, block, isGrad ? _grad : _data, value, _size);</div>
<div class="line"><a id="l00405" name="l00405"></a><span class="lineno">  405</span>    }</div>
</div>
<div class="line"><a id="l00406" name="l00406"></a><span class="lineno">  406</span> </div>
<div class="foldopen" id="foldopen00407" data-start="{" data-end="}">
<div class="line"><a id="l00407" name="l00407"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_mapped_tensor.html#a0422426fcdfec736a3773414670472a9">  407</a></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classnz_1_1data_1_1_mapped_tensor.html#a0422426fcdfec736a3773414670472a9">MappedTensor::fillMatrix</a>(value_type value, size_type batch, size_type channels, <span class="keywordtype">bool</span> isGrad) {</div>
<div class="line"><a id="l00408" name="l00408"></a><span class="lineno">  408</span>        <span class="keywordflow">if</span> (batch &gt;= _shape[0] || channels &gt;= _shape[1]) {</div>
<div class="line"><a id="l00409" name="l00409"></a><span class="lineno">  409</span>            <span class="keywordflow">throw</span> std::invalid_argument(<span class="stringliteral">&quot;Invalid batch or channels&quot;</span>);</div>
<div class="line"><a id="l00410" name="l00410"></a><span class="lineno">  410</span>        }</div>
<div class="line"><a id="l00411" name="l00411"></a><span class="lineno">  411</span>        <span class="keywordflow">if</span> (isGrad &amp;&amp; !_requires_grad) {</div>
<div class="line"><a id="l00412" name="l00412"></a><span class="lineno">  412</span>            <span class="keywordflow">throw</span> std::invalid_argument(</div>
<div class="line"><a id="l00413" name="l00413"></a><span class="lineno">  413</span>                <span class="stringliteral">&quot;Gradient filling is not allowed for tensors that do not require gradients.&quot;</span>);</div>
<div class="line"><a id="l00414" name="l00414"></a><span class="lineno">  414</span>        }</div>
<div class="line"><a id="l00415" name="l00415"></a><span class="lineno">  415</span>        <span class="keyword">const</span> dim3 block(512);</div>
<div class="line"><a id="l00416" name="l00416"></a><span class="lineno">  416</span>        <span class="keyword">const</span> dim3 grid((_shape[2] * _shape[3] + block.x - 1) / block.x);</div>
<div class="line"><a id="l00417" name="l00417"></a><span class="lineno">  417</span>        <span class="keyword">const</span> <span class="keyword">auto</span> offset = batch * _shape.<a class="code hl_function" href="classnz_1_1data_1_1_dimension.html#a4831fea5aaf7dbad3578d3fa8e55aef1">getStride</a>(0) + channels * _shape.<a class="code hl_function" href="classnz_1_1data_1_1_dimension.html#a4831fea5aaf7dbad3578d3fa8e55aef1">getStride</a>(1);</div>
<div class="line"><a id="l00418" name="l00418"></a><span class="lineno">  418</span>        <a class="code hl_function" href="namespacenz_1_1krnl.html#ad136c8a6560a5305984ce0a31bea71bf">krnl::Fill</a>(grid, block, (isGrad ? _grad : _data), value, _shape[2] * _shape[3], offset);</div>
<div class="line"><a id="l00419" name="l00419"></a><span class="lineno">  419</span>    }</div>
</div>
<div class="line"><a id="l00420" name="l00420"></a><span class="lineno">  420</span> </div>
<div class="foldopen" id="foldopen00421" data-start="{" data-end="}">
<div class="line"><a id="l00421" name="l00421"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_mapped_tensor.html#afae0e7076636cd708762f57f10f03952">  421</a></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classnz_1_1data_1_1_mapped_tensor.html#afae0e7076636cd708762f57f10f03952">MappedTensor::transpose</a>() {</div>
<div class="line"><a id="l00422" name="l00422"></a><span class="lineno">  422</span>        <span class="keyword">const</span> dim3 block(TILE_SIZE, TILE_SIZE);</div>
<div class="line"><a id="l00423" name="l00423"></a><span class="lineno">  423</span>        <span class="keyword">const</span> dim3 grid((_shape[2] + block.x - 1) / block.x, (_shape[3] + block.y - 1) / block.y);</div>
<div class="line"><a id="l00424" name="l00424"></a><span class="lineno">  424</span>        value_type* temp;</div>
<div class="line"><a id="l00425" name="l00425"></a><span class="lineno">  425</span>        std::vector&lt;size_t&gt; offset;</div>
<div class="line"><a id="l00426" name="l00426"></a><span class="lineno">  426</span>        CHECK(cudaMallocHost(&amp;temp, _size * <span class="keyword">sizeof</span>(value_type)));</div>
<div class="line"><a id="l00427" name="l00427"></a><span class="lineno">  427</span>        <span class="keywordflow">for</span> (<span class="keyword">auto</span> i = 0; i &lt; _shape[0]; i += 1) {</div>
<div class="line"><a id="l00428" name="l00428"></a><span class="lineno">  428</span>            <span class="keywordflow">for</span> (<span class="keyword">auto</span> j = 0; j &lt; _shape[1]; j += 1) {</div>
<div class="line"><a id="l00429" name="l00429"></a><span class="lineno">  429</span>                offset.push_back(i * _shape.getStride(0) + j * _shape.getStride(1));</div>
<div class="line"><a id="l00430" name="l00430"></a><span class="lineno">  430</span>            }</div>
<div class="line"><a id="l00431" name="l00431"></a><span class="lineno">  431</span>        }</div>
<div class="line"><a id="l00432" name="l00432"></a><span class="lineno">  432</span>        <a class="code hl_function" href="namespacenz_1_1krnl.html#afe3f38f788c735b7eb718443eb0fd094">krnl::Transpose</a>(grid, block, _data, temp, _shape[2], _shape[3], offset);</div>
<div class="line"><a id="l00433" name="l00433"></a><span class="lineno">  433</span>        <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab6803232b9c08d9282b16322a6c7b8a9">freeHost</a>(_data);</div>
<div class="line"><a id="l00434" name="l00434"></a><span class="lineno">  434</span>        _data = temp;</div>
<div class="line"><a id="l00435" name="l00435"></a><span class="lineno">  435</span>        <span class="keywordflow">if</span> (_requires_grad) {</div>
<div class="line"><a id="l00436" name="l00436"></a><span class="lineno">  436</span>            value_type* tempGrad;</div>
<div class="line"><a id="l00437" name="l00437"></a><span class="lineno">  437</span>            CHECK(cudaMallocHost(&amp;tempGrad, _size * <span class="keyword">sizeof</span>(value_type)));</div>
<div class="line"><a id="l00438" name="l00438"></a><span class="lineno">  438</span>            <a class="code hl_function" href="namespacenz_1_1krnl.html#afe3f38f788c735b7eb718443eb0fd094">krnl::Transpose</a>(grid, block, _grad, tempGrad, _shape[2], _shape[3], offset);</div>
<div class="line"><a id="l00439" name="l00439"></a><span class="lineno">  439</span>            <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab6803232b9c08d9282b16322a6c7b8a9">freeHost</a>(_grad);</div>
<div class="line"><a id="l00440" name="l00440"></a><span class="lineno">  440</span>            _grad = tempGrad;</div>
<div class="line"><a id="l00441" name="l00441"></a><span class="lineno">  441</span>        }</div>
<div class="line"><a id="l00442" name="l00442"></a><span class="lineno">  442</span>        std::swap(_shape[2], _shape[3]);</div>
<div class="line"><a id="l00443" name="l00443"></a><span class="lineno">  443</span>        _shape.updateStride();</div>
<div class="line"><a id="l00444" name="l00444"></a><span class="lineno">  444</span>    }</div>
</div>
<div class="line"><a id="l00445" name="l00445"></a><span class="lineno">  445</span> </div>
<div class="foldopen" id="foldopen00446" data-start="{" data-end="}">
<div class="line"><a id="l00446" name="l00446"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_mapped_tensor.html#a42529629ec686811b950f50b84cffd62">  446</a></span>    <a class="code hl_class" href="classnz_1_1data_1_1_mapped_tensor.html">MappedTensor</a> <a class="code hl_function" href="classnz_1_1data_1_1_mapped_tensor.html#a42529629ec686811b950f50b84cffd62">MappedTensor::operator+</a>(<span class="keyword">const</span> <a class="code hl_class" href="classnz_1_1data_1_1_mapped_tensor.html">MappedTensor</a>&amp; other)<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00447" name="l00447"></a><span class="lineno">  447</span>        <a class="code hl_class" href="classnz_1_1data_1_1_mapped_tensor.html">MappedTensor</a> result(_shape.<a class="code hl_function" href="classnz_1_1data_1_1_dimension.html#ab4f9f0cec97b8e579b62ccb37975de3c">Broadcast</a>(other._shape), _requires_grad || other._requires_grad);</div>
<div class="line"><a id="l00448" name="l00448"></a><span class="lineno">  448</span>        <a class="code hl_function" href="namespacenz_1_1data.html#a8cf4ac2437dd67698684169bebb225d4">tensorMatrixAdd</a>(result, *<span class="keyword">this</span>, other);</div>
<div class="line"><a id="l00449" name="l00449"></a><span class="lineno">  449</span>        <span class="keywordflow">return</span> result;</div>
<div class="line"><a id="l00450" name="l00450"></a><span class="lineno">  450</span>    }</div>
</div>
<div class="line"><a id="l00451" name="l00451"></a><span class="lineno">  451</span> </div>
<div class="foldopen" id="foldopen00452" data-start="{" data-end="}">
<div class="line"><a id="l00452" name="l00452"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_mapped_tensor.html#ab1346540c19b9ab6f416514cd2164c8b">  452</a></span>    <a class="code hl_class" href="classnz_1_1data_1_1_mapped_tensor.html">MappedTensor</a> <a class="code hl_function" href="classnz_1_1data_1_1_mapped_tensor.html#aeb5887e43ba46f43ab2ed5ed78a62de1">MappedTensor::operator-</a>(<span class="keyword">const</span> <a class="code hl_class" href="classnz_1_1data_1_1_mapped_tensor.html">MappedTensor</a>&amp; other)<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00453" name="l00453"></a><span class="lineno">  453</span>        <a class="code hl_class" href="classnz_1_1data_1_1_mapped_tensor.html">MappedTensor</a> result(_shape.<a class="code hl_function" href="classnz_1_1data_1_1_dimension.html#ab4f9f0cec97b8e579b62ccb37975de3c">Broadcast</a>(other._shape), _requires_grad || other._requires_grad);</div>
<div class="line"><a id="l00454" name="l00454"></a><span class="lineno">  454</span>        <a class="code hl_function" href="namespacenz_1_1data.html#a7503b6894e8052ed54eb169550d135c0">tensorMatrixSub</a>(result, *<span class="keyword">this</span>, other);</div>
<div class="line"><a id="l00455" name="l00455"></a><span class="lineno">  455</span>        <span class="keywordflow">return</span> result;</div>
<div class="line"><a id="l00456" name="l00456"></a><span class="lineno">  456</span>    }</div>
</div>
<div class="line"><a id="l00457" name="l00457"></a><span class="lineno">  457</span> </div>
<div class="foldopen" id="foldopen00458" data-start="{" data-end="}">
<div class="line"><a id="l00458" name="l00458"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_mapped_tensor.html#a7a6bc3862735542300e7f3ecd2df887f">  458</a></span>    <a class="code hl_class" href="classnz_1_1data_1_1_mapped_tensor.html">MappedTensor</a> <a class="code hl_function" href="classnz_1_1data_1_1_mapped_tensor.html#a7a6bc3862735542300e7f3ecd2df887f">MappedTensor::operator*</a>(<span class="keyword">const</span> <a class="code hl_class" href="classnz_1_1data_1_1_mapped_tensor.html">MappedTensor</a>&amp; other)<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00459" name="l00459"></a><span class="lineno">  459</span>        <a class="code hl_class" href="classnz_1_1data_1_1_mapped_tensor.html">MappedTensor</a> result({</div>
<div class="line"><a id="l00460" name="l00460"></a><span class="lineno">  460</span>                                std::max(_shape.<a class="code hl_function" href="classnz_1_1data_1_1_dimension.html#acc472e84b4c44f649f34b6fbb0eeacf7">N</a>(), other._shape.<a class="code hl_function" href="classnz_1_1data_1_1_dimension.html#acc472e84b4c44f649f34b6fbb0eeacf7">N</a>()), std::max(_shape.<a class="code hl_function" href="classnz_1_1data_1_1_dimension.html#ae1e87c4a462dd60e02821aa27ffc7e09">C</a>(), other._shape.<a class="code hl_function" href="classnz_1_1data_1_1_dimension.html#ae1e87c4a462dd60e02821aa27ffc7e09">C</a>()),</div>
<div class="line"><a id="l00461" name="l00461"></a><span class="lineno">  461</span>                                _shape.<a class="code hl_function" href="classnz_1_1data_1_1_dimension.html#a7eb3acc882c48e775c418d97f709240f">H</a>(), other._shape.<a class="code hl_function" href="classnz_1_1data_1_1_dimension.html#a65773c675476dfea3f06b30f21ebbedd">W</a>()</div>
<div class="line"><a id="l00462" name="l00462"></a><span class="lineno">  462</span>                            }, _requires_grad || other._requires_grad);</div>
<div class="line"><a id="l00463" name="l00463"></a><span class="lineno">  463</span>        <a class="code hl_function" href="namespacenz_1_1data.html#a5a166a472b887c45fde9e5815f072234">tensorGeneralMatrixMul</a>(result, *<span class="keyword">this</span>, other);</div>
<div class="line"><a id="l00464" name="l00464"></a><span class="lineno">  464</span>        <span class="keywordflow">return</span> result;</div>
<div class="line"><a id="l00465" name="l00465"></a><span class="lineno">  465</span>    }</div>
</div>
<div class="line"><a id="l00466" name="l00466"></a><span class="lineno">  466</span> </div>
<div class="foldopen" id="foldopen00467" data-start="{" data-end="}">
<div class="line"><a id="l00467" name="l00467"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_mapped_tensor.html#aeb5887e43ba46f43ab2ed5ed78a62de1">  467</a></span>    <a class="code hl_class" href="classnz_1_1data_1_1_mapped_tensor.html">MappedTensor</a> <a class="code hl_function" href="classnz_1_1data_1_1_mapped_tensor.html#aeb5887e43ba46f43ab2ed5ed78a62de1">MappedTensor::operator-</a>()<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00468" name="l00468"></a><span class="lineno">  468</span>        <a class="code hl_class" href="classnz_1_1data_1_1_mapped_tensor.html">MappedTensor</a> result(_shape, _requires_grad);</div>
<div class="line"><a id="l00469" name="l00469"></a><span class="lineno">  469</span>        <span class="keyword">const</span> dim3 block(512);</div>
<div class="line"><a id="l00470" name="l00470"></a><span class="lineno">  470</span>        <span class="keyword">const</span> dim3 grid((_size + block.x - 1) / block.x);</div>
<div class="line"><a id="l00471" name="l00471"></a><span class="lineno">  471</span>        <a class="code hl_function" href="namespacenz_1_1krnl.html#af7069a420e81babb49b1bc009333d053">krnl::Negation</a>(grid, block, result._data, _data, _size);</div>
<div class="line"><a id="l00472" name="l00472"></a><span class="lineno">  472</span>        <span class="keywordflow">return</span> result;</div>
<div class="line"><a id="l00473" name="l00473"></a><span class="lineno">  473</span>    }</div>
</div>
<div class="line"><a id="l00474" name="l00474"></a><span class="lineno">  474</span> </div>
<div class="foldopen" id="foldopen00475" data-start="{" data-end="}">
<div class="line"><a id="l00475" name="l00475"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_mapped_tensor.html#a79064970ec839800d23d8fc890e0d1f9">  475</a></span>    <span class="keywordtype">bool</span> <a class="code hl_function" href="classnz_1_1data_1_1_mapped_tensor.html#a79064970ec839800d23d8fc890e0d1f9">MappedTensor::operator==</a>(<span class="keyword">const</span> <a class="code hl_class" href="classnz_1_1data_1_1_mapped_tensor.html">MappedTensor</a>&amp; other)<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00476" name="l00476"></a><span class="lineno">  476</span>        <span class="keywordflow">if</span> (_requires_grad != other._requires_grad) {</div>
<div class="line"><a id="l00477" name="l00477"></a><span class="lineno">  477</span>            <span class="keywordflow">return</span> <span class="keyword">false</span>;</div>
<div class="line"><a id="l00478" name="l00478"></a><span class="lineno">  478</span>        }</div>
<div class="line"><a id="l00479" name="l00479"></a><span class="lineno">  479</span>        <span class="keywordflow">if</span> (_shape != other._shape) {</div>
<div class="line"><a id="l00480" name="l00480"></a><span class="lineno">  480</span>            <span class="keywordflow">return</span> <span class="keyword">false</span>;</div>
<div class="line"><a id="l00481" name="l00481"></a><span class="lineno">  481</span>        }</div>
<div class="line"><a id="l00482" name="l00482"></a><span class="lineno">  482</span>        <span class="keyword">constexpr</span> value_type abs_epsilon = 1e-6f;</div>
<div class="line"><a id="l00483" name="l00483"></a><span class="lineno">  483</span>        <span class="keyword">constexpr</span> value_type rel_epsilon = 1e-5f;</div>
<div class="line"><a id="l00484" name="l00484"></a><span class="lineno">  484</span>        this-&gt;<a class="code hl_function" href="classnz_1_1data_1_1_mapped_tensor.html#a5134b8821da18631dd9a9b9417b0ba5e">sync</a>();</div>
<div class="line"><a id="l00485" name="l00485"></a><span class="lineno">  485</span>        other.<a class="code hl_function" href="classnz_1_1data_1_1_mapped_tensor.html#a5134b8821da18631dd9a9b9417b0ba5e">sync</a>();</div>
<div class="line"><a id="l00486" name="l00486"></a><span class="lineno">  486</span>        <span class="keywordflow">for</span> (<span class="keyword">auto</span> i = 0; i &lt; _size; i++) {</div>
<div class="line"><a id="l00487" name="l00487"></a><span class="lineno">  487</span>            <span class="keyword">const</span> <span class="keyword">auto</span> a = _data[i];</div>
<div class="line"><a id="l00488" name="l00488"></a><span class="lineno">  488</span>            <span class="keyword">const</span> <span class="keyword">auto</span> b = other._data[i];</div>
<div class="line"><a id="l00489" name="l00489"></a><span class="lineno">  489</span>            <span class="keyword">const</span> <span class="keyword">auto</span> diff = std::abs(a - b);</div>
<div class="line"><a id="l00490" name="l00490"></a><span class="lineno">  490</span>            <span class="keywordflow">if</span> (diff &gt; std::max(rel_epsilon * std::max(std::abs(a), std::abs(b)), abs_epsilon)) {</div>
<div class="line"><a id="l00491" name="l00491"></a><span class="lineno">  491</span>                <span class="keywordflow">return</span> <span class="keyword">false</span>;</div>
<div class="line"><a id="l00492" name="l00492"></a><span class="lineno">  492</span>            }</div>
<div class="line"><a id="l00493" name="l00493"></a><span class="lineno">  493</span>        }</div>
<div class="line"><a id="l00494" name="l00494"></a><span class="lineno">  494</span>        <span class="keywordflow">if</span> (_requires_grad) {</div>
<div class="line"><a id="l00495" name="l00495"></a><span class="lineno">  495</span>            <span class="keywordflow">for</span> (<span class="keyword">auto</span> i = 0; i &lt; _size; i++) {</div>
<div class="line"><a id="l00496" name="l00496"></a><span class="lineno">  496</span>                <span class="keyword">const</span> <span class="keyword">auto</span> a = _grad[i];</div>
<div class="line"><a id="l00497" name="l00497"></a><span class="lineno">  497</span>                <span class="keyword">const</span> <span class="keyword">auto</span> b = other._grad[i];</div>
<div class="line"><a id="l00498" name="l00498"></a><span class="lineno">  498</span>                <span class="keyword">const</span> <span class="keyword">auto</span> diff = std::abs(a - b);</div>
<div class="line"><a id="l00499" name="l00499"></a><span class="lineno">  499</span>                <span class="keywordflow">if</span> (diff &gt; std::max(rel_epsilon * std::max(std::abs(a), std::abs(b)), abs_epsilon)) {</div>
<div class="line"><a id="l00500" name="l00500"></a><span class="lineno">  500</span>                    <span class="keywordflow">return</span> <span class="keyword">false</span>;</div>
<div class="line"><a id="l00501" name="l00501"></a><span class="lineno">  501</span>                }</div>
<div class="line"><a id="l00502" name="l00502"></a><span class="lineno">  502</span>            }</div>
<div class="line"><a id="l00503" name="l00503"></a><span class="lineno">  503</span>        }</div>
<div class="line"><a id="l00504" name="l00504"></a><span class="lineno">  504</span>        <span class="keywordflow">return</span> <span class="keyword">true</span>;</div>
<div class="line"><a id="l00505" name="l00505"></a><span class="lineno">  505</span>    }</div>
</div>
<div class="line"><a id="l00506" name="l00506"></a><span class="lineno">  506</span> </div>
<div class="foldopen" id="foldopen00507" data-start="{" data-end="}">
<div class="line"><a id="l00507" name="l00507"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_mapped_tensor.html#a96696ca7de5d5d3b7e0ba834a80d541d">  507</a></span>    <span class="keywordtype">bool</span> <a class="code hl_function" href="classnz_1_1data_1_1_mapped_tensor.html#a96696ca7de5d5d3b7e0ba834a80d541d">MappedTensor::operator!=</a>(<span class="keyword">const</span> <a class="code hl_class" href="classnz_1_1data_1_1_mapped_tensor.html">MappedTensor</a>&amp; other)<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00508" name="l00508"></a><span class="lineno">  508</span>        <span class="keywordflow">return</span> !(*<span class="keyword">this</span> == other);</div>
<div class="line"><a id="l00509" name="l00509"></a><span class="lineno">  509</span>    }</div>
</div>
<div class="line"><a id="l00510" name="l00510"></a><span class="lineno">  510</span> </div>
<div class="foldopen" id="foldopen00511" data-start="{" data-end="}">
<div class="line"><a id="l00511" name="l00511"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_mapped_tensor.html#a51ecfec4ea055d7e4c11c4540c3fd996">  511</a></span>    <a class="code hl_class" href="classnz_1_1data_1_1_mapped_tensor.html">MappedTensor</a> <a class="code hl_function" href="classnz_1_1data_1_1_mapped_tensor.html#a51ecfec4ea055d7e4c11c4540c3fd996">MappedTensor::operator/</a>(<span class="keyword">const</span> <a class="code hl_class" href="classnz_1_1data_1_1_mapped_tensor.html">MappedTensor</a>&amp; other)<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00512" name="l00512"></a><span class="lineno">  512</span>        <a class="code hl_class" href="classnz_1_1data_1_1_mapped_tensor.html">MappedTensor</a> result(_shape.<a class="code hl_function" href="classnz_1_1data_1_1_dimension.html#ab4f9f0cec97b8e579b62ccb37975de3c">Broadcast</a>(other._shape), _requires_grad || other._requires_grad);</div>
<div class="line"><a id="l00513" name="l00513"></a><span class="lineno">  513</span>        <a class="code hl_function" href="namespacenz_1_1data.html#a1da5cd018533919ed5a750b14c7d6d71">tensorElementwiseDivide</a>(result, *<span class="keyword">this</span>, other);</div>
<div class="line"><a id="l00514" name="l00514"></a><span class="lineno">  514</span>        <span class="keywordflow">return</span> result;</div>
<div class="line"><a id="l00515" name="l00515"></a><span class="lineno">  515</span>    }</div>
</div>
<div class="line"><a id="l00516" name="l00516"></a><span class="lineno">  516</span> </div>
<div class="foldopen" id="foldopen00517" data-start="{" data-end="}">
<div class="line"><a id="l00517" name="l00517"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_mapped_tensor.html#a33ec3056e0ae0d2088aa83a7f31f6cec">  517</a></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classnz_1_1data_1_1_mapped_tensor.html#a33ec3056e0ae0d2088aa83a7f31f6cec">MappedTensor::recip</a>() {</div>
<div class="line"><a id="l00518" name="l00518"></a><span class="lineno">  518</span>        <span class="keyword">const</span> dim3 block(512);</div>
<div class="line"><a id="l00519" name="l00519"></a><span class="lineno">  519</span>        <span class="keyword">const</span> dim3 grid((_size + block.x - 1) / block.x);</div>
<div class="line"><a id="l00520" name="l00520"></a><span class="lineno">  520</span>        value_type* temp;</div>
<div class="line"><a id="l00521" name="l00521"></a><span class="lineno">  521</span>        CHECK(cudaMallocHost(&amp;temp, _size * <span class="keyword">sizeof</span>(value_type)));</div>
<div class="line"><a id="l00522" name="l00522"></a><span class="lineno">  522</span>        <a class="code hl_function" href="namespacenz_1_1krnl.html#adc047e65307dbc711235f637227b7d10">krnl::Recip</a>(grid, block, temp, _data, _size);</div>
<div class="line"><a id="l00523" name="l00523"></a><span class="lineno">  523</span>        <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab6803232b9c08d9282b16322a6c7b8a9">freeHost</a>(_data);</div>
<div class="line"><a id="l00524" name="l00524"></a><span class="lineno">  524</span>        _data = temp;</div>
<div class="line"><a id="l00525" name="l00525"></a><span class="lineno">  525</span>    }</div>
</div>
<div class="line"><a id="l00526" name="l00526"></a><span class="lineno">  526</span> </div>
<div class="foldopen" id="foldopen00527" data-start="{" data-end="}">
<div class="line"><a id="l00527" name="l00527"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_mapped_tensor.html#afabf30bc35435f1101a61afa77b026ad">  527</a></span>    MappedTensor::value_type <a class="code hl_function" href="classnz_1_1data_1_1_mapped_tensor.html#afabf30bc35435f1101a61afa77b026ad">MappedTensor::sum</a>()<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00528" name="l00528"></a><span class="lineno">  528</span>        <span class="keyword">const</span> dim3 block(256);</div>
<div class="line"><a id="l00529" name="l00529"></a><span class="lineno">  529</span>        <span class="keyword">const</span> dim3 grid((_size + block.x - 1) / block.x);</div>
<div class="line"><a id="l00530" name="l00530"></a><span class="lineno">  530</span>        value_type* dData;</div>
<div class="line"><a id="l00531" name="l00531"></a><span class="lineno">  531</span>        CHECK(cudaMallocHost(&amp;dData, grid.x * <span class="keyword">sizeof</span>(value_type)));</div>
<div class="line"><a id="l00532" name="l00532"></a><span class="lineno">  532</span>        <a class="code hl_function" href="namespacenz_1_1krnl.html#a1ae846a65c2f5b83cd1b9fc61b877854">krnl::Summation</a>(grid, block, block.x / WARP_SIZE * <span class="keyword">sizeof</span>(<span class="keywordtype">float</span>), dData, _data, _size);</div>
<div class="line"><a id="l00533" name="l00533"></a><span class="lineno">  533</span>        value_type result = 0;</div>
<div class="line"><a id="l00534" name="l00534"></a><span class="lineno">  534</span>        <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#abe439fa00c0bd369c0b2345b095ed5af">syncData</a>(dData);</div>
<div class="line"><a id="l00535" name="l00535"></a><span class="lineno">  535</span>        <span class="keywordflow">for</span> (<span class="keyword">auto</span> i = 0; i &lt; grid.x; ++i) {</div>
<div class="line"><a id="l00536" name="l00536"></a><span class="lineno">  536</span>            result += dData[i];</div>
<div class="line"><a id="l00537" name="l00537"></a><span class="lineno">  537</span>        }</div>
<div class="line"><a id="l00538" name="l00538"></a><span class="lineno">  538</span>        <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab6803232b9c08d9282b16322a6c7b8a9">freeHost</a>(dData);</div>
<div class="line"><a id="l00539" name="l00539"></a><span class="lineno">  539</span>        <span class="keywordflow">return</span> result;</div>
<div class="line"><a id="l00540" name="l00540"></a><span class="lineno">  540</span>    }</div>
</div>
<div class="line"><a id="l00541" name="l00541"></a><span class="lineno">  541</span> </div>
<div class="foldopen" id="foldopen00542" data-start="{" data-end="}">
<div class="line"><a id="l00542" name="l00542"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_mapped_tensor.html#a75956c8f5cfc2ea1e7e7efe90e13c9c2">  542</a></span>    MappedTensor::value_type <a class="code hl_function" href="classnz_1_1data_1_1_mapped_tensor.html#afabf30bc35435f1101a61afa77b026ad">MappedTensor::sum</a>(<span class="keyword">const</span> <span class="keywordtype">size_t</span> batch, <span class="keyword">const</span> <span class="keywordtype">size_t</span> channel)<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00543" name="l00543"></a><span class="lineno">  543</span>        <span class="keywordflow">if</span> (batch &gt;= _shape[0] || channel &gt;= _shape[1]) {</div>
<div class="line"><a id="l00544" name="l00544"></a><span class="lineno">  544</span>            <span class="keywordflow">throw</span> std::invalid_argument(<span class="stringliteral">&quot;Invalid position&quot;</span>);</div>
<div class="line"><a id="l00545" name="l00545"></a><span class="lineno">  545</span>        }</div>
<div class="line"><a id="l00546" name="l00546"></a><span class="lineno">  546</span>        <span class="keyword">const</span> <span class="keyword">auto</span> <a class="code hl_function" href="classnz_1_1data_1_1_mapped_tensor.html#aa144f9f3b76741e5d3fb3b5df3cc3b42">size</a> = _shape[2] * _shape[3];</div>
<div class="line"><a id="l00547" name="l00547"></a><span class="lineno">  547</span>        <span class="keyword">const</span> dim3 block(256);</div>
<div class="line"><a id="l00548" name="l00548"></a><span class="lineno">  548</span>        <span class="keyword">const</span> dim3 grid((<a class="code hl_function" href="classnz_1_1data_1_1_mapped_tensor.html#aa144f9f3b76741e5d3fb3b5df3cc3b42">size</a> + block.x - 1) / block.x);</div>
<div class="line"><a id="l00549" name="l00549"></a><span class="lineno">  549</span>        value_type* dData;</div>
<div class="line"><a id="l00550" name="l00550"></a><span class="lineno">  550</span>        cudaMallocHost(&amp;dData, grid.x * <span class="keyword">sizeof</span>(value_type));</div>
<div class="line"><a id="l00551" name="l00551"></a><span class="lineno">  551</span>        <span class="keyword">const</span> <span class="keyword">auto</span> offset = batch * _shape.getStride(0) + channel * _shape.getStride(1);</div>
<div class="line"><a id="l00552" name="l00552"></a><span class="lineno">  552</span>        <a class="code hl_function" href="namespacenz_1_1krnl.html#a1ae846a65c2f5b83cd1b9fc61b877854">krnl::Summation</a>(grid, block, block.x / WARP_SIZE * <span class="keyword">sizeof</span>(<span class="keywordtype">float</span>), dData, _data, <a class="code hl_function" href="classnz_1_1data_1_1_mapped_tensor.html#aa144f9f3b76741e5d3fb3b5df3cc3b42">size</a>, offset);</div>
<div class="line"><a id="l00553" name="l00553"></a><span class="lineno">  553</span>        <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#abe439fa00c0bd369c0b2345b095ed5af">syncData</a>(dData);</div>
<div class="line"><a id="l00554" name="l00554"></a><span class="lineno">  554</span>        value_type result = 0;</div>
<div class="line"><a id="l00555" name="l00555"></a><span class="lineno">  555</span>        <span class="keywordflow">for</span> (<span class="keyword">auto</span> i = 0; i &lt; grid.x; ++i) {</div>
<div class="line"><a id="l00556" name="l00556"></a><span class="lineno">  556</span>            result += dData[i];</div>
<div class="line"><a id="l00557" name="l00557"></a><span class="lineno">  557</span>        }</div>
<div class="line"><a id="l00558" name="l00558"></a><span class="lineno">  558</span>        <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab6803232b9c08d9282b16322a6c7b8a9">freeHost</a>(dData);</div>
<div class="line"><a id="l00559" name="l00559"></a><span class="lineno">  559</span>        <span class="keywordflow">return</span> result;</div>
<div class="line"><a id="l00560" name="l00560"></a><span class="lineno">  560</span>    }</div>
</div>
<div class="line"><a id="l00561" name="l00561"></a><span class="lineno">  561</span> </div>
<div class="foldopen" id="foldopen00562" data-start="{" data-end="}">
<div class="line"><a id="l00562" name="l00562"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_mapped_tensor.html#ad4b349b1cbc2fef18f4b450f4d11377a">  562</a></span>    MappedTensor::value_type <a class="code hl_function" href="classnz_1_1data_1_1_mapped_tensor.html#ad4b349b1cbc2fef18f4b450f4d11377a">MappedTensor::expSum</a>()<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00563" name="l00563"></a><span class="lineno">  563</span>        <span class="keyword">const</span> dim3 block(256);</div>
<div class="line"><a id="l00564" name="l00564"></a><span class="lineno">  564</span>        <span class="keyword">const</span> dim3 grid((_size + block.x - 1) / block.x);</div>
<div class="line"><a id="l00565" name="l00565"></a><span class="lineno">  565</span>        value_type* dData;</div>
<div class="line"><a id="l00566" name="l00566"></a><span class="lineno">  566</span>        CHECK(cudaMallocHost(&amp;dData, grid.x * <span class="keyword">sizeof</span>(value_type)));</div>
<div class="line"><a id="l00567" name="l00567"></a><span class="lineno">  567</span>        <a class="code hl_function" href="namespacenz_1_1krnl.html#a51a5ff3c8cc2c3051fddf32de294b467">krnl::SummationExp</a>(grid, block, block.x / WARP_SIZE * <span class="keyword">sizeof</span>(<span class="keywordtype">float</span>), dData, _data, _size);</div>
<div class="line"><a id="l00568" name="l00568"></a><span class="lineno">  568</span>        <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#abe439fa00c0bd369c0b2345b095ed5af">syncData</a>(dData);</div>
<div class="line"><a id="l00569" name="l00569"></a><span class="lineno">  569</span>        value_type result = 0;</div>
<div class="line"><a id="l00570" name="l00570"></a><span class="lineno">  570</span>        <span class="keywordflow">for</span> (<span class="keyword">auto</span> i = 0; i &lt; grid.x; ++i) {</div>
<div class="line"><a id="l00571" name="l00571"></a><span class="lineno">  571</span>            result += dData[i];</div>
<div class="line"><a id="l00572" name="l00572"></a><span class="lineno">  572</span>        }</div>
<div class="line"><a id="l00573" name="l00573"></a><span class="lineno">  573</span>        <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab6803232b9c08d9282b16322a6c7b8a9">freeHost</a>(dData);</div>
<div class="line"><a id="l00574" name="l00574"></a><span class="lineno">  574</span>        <span class="keywordflow">return</span> result;</div>
<div class="line"><a id="l00575" name="l00575"></a><span class="lineno">  575</span>    }</div>
</div>
<div class="line"><a id="l00576" name="l00576"></a><span class="lineno">  576</span> </div>
<div class="foldopen" id="foldopen00577" data-start="{" data-end="}">
<div class="line"><a id="l00577" name="l00577"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_mapped_tensor.html#abc8c0fb245f15bda119159aa07109acf">  577</a></span>    MappedTensor::value_type <a class="code hl_function" href="classnz_1_1data_1_1_mapped_tensor.html#ad4b349b1cbc2fef18f4b450f4d11377a">MappedTensor::expSum</a>(<span class="keyword">const</span> <span class="keywordtype">size_t</span> batch, <span class="keyword">const</span> <span class="keywordtype">size_t</span> channel)<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00578" name="l00578"></a><span class="lineno">  578</span>        <span class="keywordflow">if</span> (batch &gt;= _shape[0] || channel &gt;= _shape[1]) {</div>
<div class="line"><a id="l00579" name="l00579"></a><span class="lineno">  579</span>            <span class="keywordflow">throw</span> std::invalid_argument(<span class="stringliteral">&quot;Invalid position&quot;</span>);</div>
<div class="line"><a id="l00580" name="l00580"></a><span class="lineno">  580</span>        }</div>
<div class="line"><a id="l00581" name="l00581"></a><span class="lineno">  581</span>        <span class="keyword">const</span> <span class="keyword">auto</span> <a class="code hl_function" href="classnz_1_1data_1_1_mapped_tensor.html#aa144f9f3b76741e5d3fb3b5df3cc3b42">size</a> = _shape[2] * _shape[3];</div>
<div class="line"><a id="l00582" name="l00582"></a><span class="lineno">  582</span>        <span class="keyword">const</span> dim3 block(256);</div>
<div class="line"><a id="l00583" name="l00583"></a><span class="lineno">  583</span>        <span class="keyword">const</span> dim3 grid((<a class="code hl_function" href="classnz_1_1data_1_1_mapped_tensor.html#aa144f9f3b76741e5d3fb3b5df3cc3b42">size</a> + block.x - 1) / block.x);</div>
<div class="line"><a id="l00584" name="l00584"></a><span class="lineno">  584</span>        value_type* dData;</div>
<div class="line"><a id="l00585" name="l00585"></a><span class="lineno">  585</span>        cudaMallocHost(&amp;dData, grid.x * <span class="keyword">sizeof</span>(value_type));</div>
<div class="line"><a id="l00586" name="l00586"></a><span class="lineno">  586</span>        <span class="keyword">const</span> <span class="keyword">auto</span> offset = batch * _shape.getStride(0) + channel * _shape.getStride(1);</div>
<div class="line"><a id="l00587" name="l00587"></a><span class="lineno">  587</span>        <a class="code hl_function" href="namespacenz_1_1krnl.html#a51a5ff3c8cc2c3051fddf32de294b467">krnl::SummationExp</a>(grid, block, block.x / WARP_SIZE * <span class="keyword">sizeof</span>(<span class="keywordtype">float</span>), dData, _data, <a class="code hl_function" href="classnz_1_1data_1_1_mapped_tensor.html#aa144f9f3b76741e5d3fb3b5df3cc3b42">size</a>, offset);</div>
<div class="line"><a id="l00588" name="l00588"></a><span class="lineno">  588</span>        <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#abe439fa00c0bd369c0b2345b095ed5af">syncData</a>(dData);</div>
<div class="line"><a id="l00589" name="l00589"></a><span class="lineno">  589</span>        value_type result = 0;</div>
<div class="line"><a id="l00590" name="l00590"></a><span class="lineno">  590</span>        <span class="keywordflow">for</span> (<span class="keyword">auto</span> i = 0; i &lt; grid.x; ++i) {</div>
<div class="line"><a id="l00591" name="l00591"></a><span class="lineno">  591</span>            result += dData[i];</div>
<div class="line"><a id="l00592" name="l00592"></a><span class="lineno">  592</span>        }</div>
<div class="line"><a id="l00593" name="l00593"></a><span class="lineno">  593</span>        <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab6803232b9c08d9282b16322a6c7b8a9">freeHost</a>(dData);</div>
<div class="line"><a id="l00594" name="l00594"></a><span class="lineno">  594</span>        <span class="keywordflow">return</span> result;</div>
<div class="line"><a id="l00595" name="l00595"></a><span class="lineno">  595</span>    }</div>
</div>
<div class="line"><a id="l00596" name="l00596"></a><span class="lineno">  596</span> </div>
<div class="foldopen" id="foldopen00597" data-start="{" data-end="}">
<div class="line"><a id="l00597" name="l00597"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_mapped_tensor.html#af9d1b0fa71f9bb7686cc26ae7b32be8d">  597</a></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classnz_1_1data_1_1_mapped_tensor.html#af9d1b0fa71f9bb7686cc26ae7b32be8d">MappedTensor::syncGrad</a>()<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00598" name="l00598"></a><span class="lineno">  598</span>        <span class="keywordflow">if</span> (_requires_grad) {</div>
<div class="line"><a id="l00599" name="l00599"></a><span class="lineno">  599</span>            <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#abe439fa00c0bd369c0b2345b095ed5af">syncData</a>(_grad);</div>
<div class="line"><a id="l00600" name="l00600"></a><span class="lineno">  600</span>        }</div>
<div class="line"><a id="l00601" name="l00601"></a><span class="lineno">  601</span>    }</div>
</div>
<div class="line"><a id="l00602" name="l00602"></a><span class="lineno">  602</span> </div>
<div class="foldopen" id="foldopen00603" data-start="{" data-end="}">
<div class="line"><a id="l00603" name="l00603"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_mapped_tensor.html#ae133d08d4505a30c6bd34e4a7a311f9e">  603</a></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classnz_1_1data_1_1_mapped_tensor.html#ae133d08d4505a30c6bd34e4a7a311f9e">MappedTensor::syncData</a>()<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00604" name="l00604"></a><span class="lineno">  604</span>        <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#abe439fa00c0bd369c0b2345b095ed5af">syncData</a>(_data);</div>
<div class="line"><a id="l00605" name="l00605"></a><span class="lineno">  605</span>    }</div>
</div>
<div class="line"><a id="l00606" name="l00606"></a><span class="lineno">  606</span> </div>
<div class="foldopen" id="foldopen00607" data-start="{" data-end="}">
<div class="line"><a id="l00607" name="l00607"></a><span class="lineno"><a class="line" href="classnz_1_1data_1_1_mapped_tensor.html#a5134b8821da18631dd9a9b9417b0ba5e">  607</a></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classnz_1_1data_1_1_mapped_tensor.html#a5134b8821da18631dd9a9b9417b0ba5e">MappedTensor::sync</a>()<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00608" name="l00608"></a><span class="lineno">  608</span>        <a class="code hl_function" href="classnz_1_1data_1_1_mapped_tensor.html#ae133d08d4505a30c6bd34e4a7a311f9e">syncData</a>();</div>
<div class="line"><a id="l00609" name="l00609"></a><span class="lineno">  609</span>        <a class="code hl_function" href="classnz_1_1data_1_1_mapped_tensor.html#af9d1b0fa71f9bb7686cc26ae7b32be8d">syncGrad</a>();</div>
<div class="line"><a id="l00610" name="l00610"></a><span class="lineno">  610</span>    }</div>
</div>
<div class="line"><a id="l00611" name="l00611"></a><span class="lineno">  611</span>}</div>
<div class="ttc" id="a_operation_kernels_8cuh_html"><div class="ttname"><a href="_operation_kernels_8cuh.html">OperationKernels.cuh</a></div><div class="ttdoc">CUDA Kernel Definitions for High-Performance Tensor Operations.</div></div>
<div class="ttc" id="aclassnz_1_1cu_strm_1_1_stream_manager_html_a71ad766cb2869d3dd6a3931966e81706"><div class="ttname"><a href="classnz_1_1cu_strm_1_1_stream_manager.html#a71ad766cb2869d3dd6a3931966e81706">nz::cuStrm::StreamManager::memset</a></div><div class="ttdeci">void memset(T *data, const int value, const size_t count)</div><div class="ttdoc">Asynchronously sets a block of CUDA device memory to a specified value.</div><div class="ttdef"><b>Definition</b> <a href="_stream_manager_8cuh_source.html#l00360">StreamManager.cuh:360</a></div></div>
<div class="ttc" id="aclassnz_1_1cu_strm_1_1_stream_manager_html_ab4b2eb422e0e1ee44bdfdc0eb94457ce"><div class="ttname"><a href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">nz::cuStrm::StreamManager::Instance</a></div><div class="ttdeci">static StreamManager &amp; Instance()</div><div class="ttdoc">Returns a reference to the singleton instance of the StreamManager.</div><div class="ttdef"><b>Definition</b> <a href="_stream_manager_8cuh_source.html#l00154">StreamManager.cuh:154</a></div></div>
<div class="ttc" id="aclassnz_1_1cu_strm_1_1_stream_manager_html_ab6803232b9c08d9282b16322a6c7b8a9"><div class="ttname"><a href="classnz_1_1cu_strm_1_1_stream_manager.html#ab6803232b9c08d9282b16322a6c7b8a9">nz::cuStrm::StreamManager::freeHost</a></div><div class="ttdeci">void freeHost(T *data)</div><div class="ttdoc">Frees the pinned host memory pointed to by the given pointer.</div><div class="ttdef"><b>Definition</b> <a href="_stream_manager_8cuh_source.html#l00299">StreamManager.cuh:299</a></div></div>
<div class="ttc" id="aclassnz_1_1cu_strm_1_1_stream_manager_html_abe439fa00c0bd369c0b2345b095ed5af"><div class="ttname"><a href="classnz_1_1cu_strm_1_1_stream_manager.html#abe439fa00c0bd369c0b2345b095ed5af">nz::cuStrm::StreamManager::syncData</a></div><div class="ttdeci">void syncData(T *data)</div><div class="ttdoc">Synchronizes host thread with completion events for a specific data object.</div><div class="ttdef"><b>Definition</b> <a href="_stream_manager_8cuh_source.html#l00714">StreamManager.cuh:714</a></div></div>
<div class="ttc" id="aclassnz_1_1cu_strm_1_1_stream_manager_html_afa38d5c6db0e6b48c8f74ce8ad0df2bc"><div class="ttname"><a href="classnz_1_1cu_strm_1_1_stream_manager.html#afa38d5c6db0e6b48c8f74ce8ad0df2bc">nz::cuStrm::StreamManager::memcpy</a></div><div class="ttdeci">void memcpy(T *dst, T *src, const size_t size, const cudaMemcpyKind kind)</div><div class="ttdoc">Asynchronously copies data between CUDA device and host memory based on the specified memory copy kin...</div><div class="ttdef"><b>Definition</b> <a href="_stream_manager_8cuh_source.html#l00391">StreamManager.cuh:391</a></div></div>
<div class="ttc" id="aclassnz_1_1data_1_1_dimension_html"><div class="ttname"><a href="classnz_1_1data_1_1_dimension.html">nz::data::Dimension</a></div><div class="ttdoc">Represents a multi - dimensional shape, typically used in deep learning for tensor dimensions.</div><div class="ttdef"><b>Definition</b> <a href="_dimension_8cuh_source.html#l00057">Dimension.cuh:57</a></div></div>
<div class="ttc" id="aclassnz_1_1data_1_1_dimension_html_a073622bb031999163987ccf77f8edfb2"><div class="ttname"><a href="classnz_1_1data_1_1_dimension.html#a073622bb031999163987ccf77f8edfb2">nz::data::Dimension::size</a></div><div class="ttdeci">size_t size() const</div><div class="ttdoc">Calculates the total number of elements in the Dimension object.</div><div class="ttdef"><b>Definition</b> <a href="_dimension_8cu_source.html#l00036">Dimension.cu:36</a></div></div>
<div class="ttc" id="aclassnz_1_1data_1_1_dimension_html_a4831fea5aaf7dbad3578d3fa8e55aef1"><div class="ttname"><a href="classnz_1_1data_1_1_dimension.html#a4831fea5aaf7dbad3578d3fa8e55aef1">nz::data::Dimension::getStride</a></div><div class="ttdeci">size_t getStride(size_t i) const</div><div class="ttdoc">Retrieves the stride value at a specified index within the Dimension object.</div><div class="ttdef"><b>Definition</b> <a href="_dimension_8cu_source.html#l00040">Dimension.cu:40</a></div></div>
<div class="ttc" id="aclassnz_1_1data_1_1_dimension_html_a65773c675476dfea3f06b30f21ebbedd"><div class="ttname"><a href="classnz_1_1data_1_1_dimension.html#a65773c675476dfea3f06b30f21ebbedd">nz::data::Dimension::W</a></div><div class="ttdeci">size_t W() const</div><div class="ttdoc">Retrieves the value of the 'w' dimension.</div><div class="ttdef"><b>Definition</b> <a href="_dimension_8cu_source.html#l00063">Dimension.cu:63</a></div></div>
<div class="ttc" id="aclassnz_1_1data_1_1_dimension_html_a7eb3acc882c48e775c418d97f709240f"><div class="ttname"><a href="classnz_1_1data_1_1_dimension.html#a7eb3acc882c48e775c418d97f709240f">nz::data::Dimension::H</a></div><div class="ttdeci">size_t H() const</div><div class="ttdoc">Retrieves the value of the 'h' dimension.</div><div class="ttdef"><b>Definition</b> <a href="_dimension_8cu_source.html#l00059">Dimension.cu:59</a></div></div>
<div class="ttc" id="aclassnz_1_1data_1_1_dimension_html_ab4f9f0cec97b8e579b62ccb37975de3c"><div class="ttname"><a href="classnz_1_1data_1_1_dimension.html#ab4f9f0cec97b8e579b62ccb37975de3c">nz::data::Dimension::Broadcast</a></div><div class="ttdeci">Dimension Broadcast(const Dimension &amp;other) const</div><div class="ttdoc">Performs broadcasting between two Dimension objects and returns the resulting Dimension.</div><div class="ttdef"><b>Definition</b> <a href="_dimension_8cu_source.html#l00125">Dimension.cu:125</a></div></div>
<div class="ttc" id="aclassnz_1_1data_1_1_dimension_html_acc472e84b4c44f649f34b6fbb0eeacf7"><div class="ttname"><a href="classnz_1_1data_1_1_dimension.html#acc472e84b4c44f649f34b6fbb0eeacf7">nz::data::Dimension::N</a></div><div class="ttdeci">size_t N() const</div><div class="ttdoc">Retrieves the value of the 'n' dimension.</div><div class="ttdef"><b>Definition</b> <a href="_dimension_8cu_source.html#l00051">Dimension.cu:51</a></div></div>
<div class="ttc" id="aclassnz_1_1data_1_1_dimension_html_accb260af17b2e888268e1a7d3cdccc71"><div class="ttname"><a href="classnz_1_1data_1_1_dimension.html#accb260af17b2e888268e1a7d3cdccc71">nz::data::Dimension::reshape</a></div><div class="ttdeci">bool reshape(const Dimension &amp;newShape)</div><div class="ttdoc">Attempts to reshape the current Dimension object to a new shape.</div><div class="ttdef"><b>Definition</b> <a href="_dimension_8cu_source.html#l00110">Dimension.cu:110</a></div></div>
<div class="ttc" id="aclassnz_1_1data_1_1_dimension_html_ae1e87c4a462dd60e02821aa27ffc7e09"><div class="ttname"><a href="classnz_1_1data_1_1_dimension.html#ae1e87c4a462dd60e02821aa27ffc7e09">nz::data::Dimension::C</a></div><div class="ttdeci">size_t C() const</div><div class="ttdoc">Retrieves the value of the 'c' dimension.</div><div class="ttdef"><b>Definition</b> <a href="_dimension_8cu_source.html#l00055">Dimension.cu:55</a></div></div>
<div class="ttc" id="aclassnz_1_1data_1_1_mapped_tensor_html"><div class="ttname"><a href="classnz_1_1data_1_1_mapped_tensor.html">nz::data::MappedTensor</a></div><div class="ttdoc">A class for representing multidimensional arrays in CUDA zero-copy memory, providing host-accessible ...</div><div class="ttdef"><b>Definition</b> <a href="_mapped_tensor_8cuh_source.html#l00066">MappedTensor.cuh:66</a></div></div>
<div class="ttc" id="aclassnz_1_1data_1_1_mapped_tensor_html_a0150ae0b7137a617c598d1f71d69ce1a"><div class="ttname"><a href="classnz_1_1data_1_1_mapped_tensor.html#a0150ae0b7137a617c598d1f71d69ce1a">nz::data::MappedTensor::randomize</a></div><div class="ttdeci">void randomize(size_type seed=0, bool isGrad=false) const</div><div class="ttdoc">Randomize the data or gradients of the MappedTensor using a given seed.</div><div class="ttdef"><b>Definition</b> <a href="#l00374">MappedTensor.cu:374</a></div></div>
<div class="ttc" id="aclassnz_1_1data_1_1_mapped_tensor_html_a0422426fcdfec736a3773414670472a9"><div class="ttname"><a href="classnz_1_1data_1_1_mapped_tensor.html#a0422426fcdfec736a3773414670472a9">nz::data::MappedTensor::fillMatrix</a></div><div class="ttdeci">void fillMatrix(value_type value, size_type batch, size_type channels, bool isGrad=false)</div><div class="ttdoc">Fills a specific matrix within the MappedTensor with a given value.</div><div class="ttdef"><b>Definition</b> <a href="#l00407">MappedTensor.cu:407</a></div></div>
<div class="ttc" id="aclassnz_1_1data_1_1_mapped_tensor_html_a0b348f12004c0b5eeb46f8f1d4b0bf53"><div class="ttname"><a href="classnz_1_1data_1_1_mapped_tensor.html#a0b348f12004c0b5eeb46f8f1d4b0bf53">nz::data::MappedTensor::clear</a></div><div class="ttdeci">void clear() const</div><div class="ttdoc">Clear the data stored in the MappedTensor by setting all elements to zero.</div><div class="ttdef"><b>Definition</b> <a href="#l00334">MappedTensor.cu:334</a></div></div>
<div class="ttc" id="aclassnz_1_1data_1_1_mapped_tensor_html_a33ec3056e0ae0d2088aa83a7f31f6cec"><div class="ttname"><a href="classnz_1_1data_1_1_mapped_tensor.html#a33ec3056e0ae0d2088aa83a7f31f6cec">nz::data::MappedTensor::recip</a></div><div class="ttdeci">void recip()</div><div class="ttdoc">Compute the reciprocal of each element in the MappedTensor.</div><div class="ttdef"><b>Definition</b> <a href="#l00517">MappedTensor.cu:517</a></div></div>
<div class="ttc" id="aclassnz_1_1data_1_1_mapped_tensor_html_a34906f06e918a40089db7a8936ee1608"><div class="ttname"><a href="classnz_1_1data_1_1_mapped_tensor.html#a34906f06e918a40089db7a8936ee1608">nz::data::MappedTensor::dataInject</a></div><div class="ttdeci">void dataInject(float *data, size_type size, bool isGrad=false) const</div><div class="ttdoc">Inject data into either the tensor's main data or its gradient.</div><div class="ttdef"><b>Definition</b> <a href="#l00237">MappedTensor.cu:237</a></div></div>
<div class="ttc" id="aclassnz_1_1data_1_1_mapped_tensor_html_a42529629ec686811b950f50b84cffd62"><div class="ttname"><a href="classnz_1_1data_1_1_mapped_tensor.html#a42529629ec686811b950f50b84cffd62">nz::data::MappedTensor::operator+</a></div><div class="ttdeci">MappedTensor operator+(const MappedTensor &amp;other) const</div><div class="ttdoc">Perform element-wise addition between two MappedTensors.</div><div class="ttdef"><b>Definition</b> <a href="#l00446">MappedTensor.cu:446</a></div></div>
<div class="ttc" id="aclassnz_1_1data_1_1_mapped_tensor_html_a5134b8821da18631dd9a9b9417b0ba5e"><div class="ttname"><a href="classnz_1_1data_1_1_mapped_tensor.html#a5134b8821da18631dd9a9b9417b0ba5e">nz::data::MappedTensor::sync</a></div><div class="ttdeci">void sync() const</div><div class="ttdoc">Synchronizes the tensor data and its gradient.</div><div class="ttdef"><b>Definition</b> <a href="#l00607">MappedTensor.cu:607</a></div></div>
<div class="ttc" id="aclassnz_1_1data_1_1_mapped_tensor_html_a51ecfec4ea055d7e4c11c4540c3fd996"><div class="ttname"><a href="classnz_1_1data_1_1_mapped_tensor.html#a51ecfec4ea055d7e4c11c4540c3fd996">nz::data::MappedTensor::operator/</a></div><div class="ttdeci">MappedTensor operator/(const MappedTensor &amp;other) const</div><div class="ttdoc">Perform element-wise division between two MappedTensors.</div><div class="ttdef"><b>Definition</b> <a href="#l00511">MappedTensor.cu:511</a></div></div>
<div class="ttc" id="aclassnz_1_1data_1_1_mapped_tensor_html_a54475a46dfd208a2ee8c1e403535abc8"><div class="ttname"><a href="classnz_1_1data_1_1_mapped_tensor.html#a54475a46dfd208a2ee8c1e403535abc8">nz::data::MappedTensor::MappedTensor</a></div><div class="ttdeci">MappedTensor()</div><div class="ttdoc">Constructs a default MappedTensor object.</div><div class="ttdef"><b>Definition</b> <a href="#l00100">MappedTensor.cu:100</a></div></div>
<div class="ttc" id="aclassnz_1_1data_1_1_mapped_tensor_html_a54d785fecf6466bd442989545ead6de4"><div class="ttname"><a href="classnz_1_1data_1_1_mapped_tensor.html#a54d785fecf6466bd442989545ead6de4">nz::data::MappedTensor::reshape</a></div><div class="ttdeci">void reshape(const shape_type &amp;shape)</div><div class="ttdoc">Reshape the MappedTensor to a new shape.</div><div class="ttdef"><b>Definition</b> <a href="#l00347">MappedTensor.cu:347</a></div></div>
<div class="ttc" id="aclassnz_1_1data_1_1_mapped_tensor_html_a79064970ec839800d23d8fc890e0d1f9"><div class="ttname"><a href="classnz_1_1data_1_1_mapped_tensor.html#a79064970ec839800d23d8fc890e0d1f9">nz::data::MappedTensor::operator==</a></div><div class="ttdeci">bool operator==(const MappedTensor &amp;other) const</div><div class="ttdoc">Checks if two MappedTensor objects are equal.</div><div class="ttdef"><b>Definition</b> <a href="#l00475">MappedTensor.cu:475</a></div></div>
<div class="ttc" id="aclassnz_1_1data_1_1_mapped_tensor_html_a7a6bc3862735542300e7f3ecd2df887f"><div class="ttname"><a href="classnz_1_1data_1_1_mapped_tensor.html#a7a6bc3862735542300e7f3ecd2df887f">nz::data::MappedTensor::operator*</a></div><div class="ttdeci">MappedTensor operator*(const MappedTensor &amp;other) const</div><div class="ttdoc">Perform matrix multiplication between two MappedTensors.</div><div class="ttdef"><b>Definition</b> <a href="#l00458">MappedTensor.cu:458</a></div></div>
<div class="ttc" id="aclassnz_1_1data_1_1_mapped_tensor_html_a8a42265d7c9f964c41ff43e9d8dc7bf7"><div class="ttname"><a href="classnz_1_1data_1_1_mapped_tensor.html#a8a42265d7c9f964c41ff43e9d8dc7bf7">nz::data::MappedTensor::print</a></div><div class="ttdeci">std::ostream &amp; print(std::ostream &amp;os) const</div><div class="ttdoc">Print the tensor data in a matrix-like format to an output stream.</div><div class="ttdef"><b>Definition</b> <a href="#l00268">MappedTensor.cu:268</a></div></div>
<div class="ttc" id="aclassnz_1_1data_1_1_mapped_tensor_html_a8ced8f419ebc96c0a1b58e7d271aaa0a"><div class="ttname"><a href="classnz_1_1data_1_1_mapped_tensor.html#a8ced8f419ebc96c0a1b58e7d271aaa0a">nz::data::MappedTensor::clearGrad</a></div><div class="ttdeci">void clearGrad() const</div><div class="ttdoc">Clear the gradient data of the MappedTensor if it requires gradients.</div><div class="ttdef"><b>Definition</b> <a href="#l00338">MappedTensor.cu:338</a></div></div>
<div class="ttc" id="aclassnz_1_1data_1_1_mapped_tensor_html_a91ee08c2ba51db828c54fa63549bd293"><div class="ttname"><a href="classnz_1_1data_1_1_mapped_tensor.html#a91ee08c2ba51db828c54fa63549bd293">nz::data::MappedTensor::grad</a></div><div class="ttdeci">value_type * grad() const</div><div class="ttdoc">Retrieves the gradient pointer of the MappedTensor.</div><div class="ttdef"><b>Definition</b> <a href="#l00206">MappedTensor.cu:206</a></div></div>
<div class="ttc" id="aclassnz_1_1data_1_1_mapped_tensor_html_a96696ca7de5d5d3b7e0ba834a80d541d"><div class="ttname"><a href="classnz_1_1data_1_1_mapped_tensor.html#a96696ca7de5d5d3b7e0ba834a80d541d">nz::data::MappedTensor::operator!=</a></div><div class="ttdeci">bool operator!=(const MappedTensor &amp;other) const</div><div class="ttdoc">Checks if two MappedTensor objects are not equal.</div><div class="ttdef"><b>Definition</b> <a href="#l00507">MappedTensor.cu:507</a></div></div>
<div class="ttc" id="aclassnz_1_1data_1_1_mapped_tensor_html_aa144f9f3b76741e5d3fb3b5df3cc3b42"><div class="ttname"><a href="classnz_1_1data_1_1_mapped_tensor.html#aa144f9f3b76741e5d3fb3b5df3cc3b42">nz::data::MappedTensor::size</a></div><div class="ttdeci">size_type size() const noexcept</div><div class="ttdoc">Retrieves the total number of elements in the MappedTensor.</div><div class="ttdef"><b>Definition</b> <a href="#l00213">MappedTensor.cu:213</a></div></div>
<div class="ttc" id="aclassnz_1_1data_1_1_mapped_tensor_html_aaf2087390e5524c8ff6790bd94c78e90"><div class="ttname"><a href="classnz_1_1data_1_1_mapped_tensor.html#aaf2087390e5524c8ff6790bd94c78e90">nz::data::MappedTensor::operator=</a></div><div class="ttdeci">MappedTensor &amp; operator=(const MappedTensor &amp;other)</div><div class="ttdoc">Copy assignment operator for the MappedTensor class.</div><div class="ttdef"><b>Definition</b> <a href="#l00127">MappedTensor.cu:127</a></div></div>
<div class="ttc" id="aclassnz_1_1data_1_1_mapped_tensor_html_ab54e80ac0d2ec90442b0a674004cfdbc"><div class="ttname"><a href="classnz_1_1data_1_1_mapped_tensor.html#ab54e80ac0d2ec90442b0a674004cfdbc">nz::data::MappedTensor::printGrad</a></div><div class="ttdeci">std::ostream &amp; printGrad(std::ostream &amp;os) const</div><div class="ttdoc">Print the gradient of the tensor in a matrix-like format to an output stream.</div><div class="ttdef"><b>Definition</b> <a href="#l00296">MappedTensor.cu:296</a></div></div>
<div class="ttc" id="aclassnz_1_1data_1_1_mapped_tensor_html_abb30df1c0174d5a8d9fa8911f7b3d3bc"><div class="ttname"><a href="classnz_1_1data_1_1_mapped_tensor.html#abb30df1c0174d5a8d9fa8911f7b3d3bc">nz::data::MappedTensor::operator[]</a></div><div class="ttdeci">auto operator[](size_type index) const -&gt; value_type &amp;</div><div class="ttdoc">Overload the [] operator to access an element of the MappedTensor by index.</div><div class="ttdef"><b>Definition</b> <a href="#l00326">MappedTensor.cu:326</a></div></div>
<div class="ttc" id="aclassnz_1_1data_1_1_mapped_tensor_html_abf8ddc60a224bc676dd0fad40ad2f43b"><div class="ttname"><a href="classnz_1_1data_1_1_mapped_tensor.html#abf8ddc60a224bc676dd0fad40ad2f43b">nz::data::MappedTensor::shape</a></div><div class="ttdeci">shape_type shape() const noexcept</div><div class="ttdoc">Retrieves the shape of the MappedTensor.</div><div class="ttdef"><b>Definition</b> <a href="#l00217">MappedTensor.cu:217</a></div></div>
<div class="ttc" id="aclassnz_1_1data_1_1_mapped_tensor_html_ac01f432f0ef9b7279630b5b35bc609e0"><div class="ttname"><a href="classnz_1_1data_1_1_mapped_tensor.html#ac01f432f0ef9b7279630b5b35bc609e0">nz::data::MappedTensor::end</a></div><div class="ttdeci">iterator end() const</div><div class="ttdoc">Returns an iterator pointing to the past - the - end element of the MappedTensor.</div><div class="ttdef"><b>Definition</b> <a href="#l00193">MappedTensor.cu:193</a></div></div>
<div class="ttc" id="aclassnz_1_1data_1_1_mapped_tensor_html_acf793cf0d23af29076d18cd46a6d19cf"><div class="ttname"><a href="classnz_1_1data_1_1_mapped_tensor.html#acf793cf0d23af29076d18cd46a6d19cf">nz::data::MappedTensor::fill</a></div><div class="ttdeci">void fill(value_type value, bool isGrad=false) const</div><div class="ttdoc">Fill the data or gradients of the MappedTensor with a given value.</div><div class="ttdef"><b>Definition</b> <a href="#l00397">MappedTensor.cu:397</a></div></div>
<div class="ttc" id="aclassnz_1_1data_1_1_mapped_tensor_html_ad4b349b1cbc2fef18f4b450f4d11377a"><div class="ttname"><a href="classnz_1_1data_1_1_mapped_tensor.html#ad4b349b1cbc2fef18f4b450f4d11377a">nz::data::MappedTensor::expSum</a></div><div class="ttdeci">value_type expSum() const</div><div class="ttdoc">Calculate the sum of the exponential values of all elements in the MappedTensor.</div><div class="ttdef"><b>Definition</b> <a href="#l00562">MappedTensor.cu:562</a></div></div>
<div class="ttc" id="aclassnz_1_1data_1_1_mapped_tensor_html_ad72562ee8cad3196dc9ffbdd436f74a3"><div class="ttname"><a href="classnz_1_1data_1_1_mapped_tensor.html#ad72562ee8cad3196dc9ffbdd436f74a3">nz::data::MappedTensor::data</a></div><div class="ttdeci">value_type * data() const noexcept</div><div class="ttdoc">Retrieves a pointer to the underlying data array of the MappedTensor.</div><div class="ttdef"><b>Definition</b> <a href="#l00202">MappedTensor.cu:202</a></div></div>
<div class="ttc" id="aclassnz_1_1data_1_1_mapped_tensor_html_ad76dcba2cd22c53b8c4815da113dcb56"><div class="ttname"><a href="classnz_1_1data_1_1_mapped_tensor.html#ad76dcba2cd22c53b8c4815da113dcb56">nz::data::MappedTensor::begin</a></div><div class="ttdeci">iterator begin() const</div><div class="ttdoc">Returns an iterator pointing to the first element of the MappedTensor.</div><div class="ttdef"><b>Definition</b> <a href="#l00188">MappedTensor.cu:188</a></div></div>
<div class="ttc" id="aclassnz_1_1data_1_1_mapped_tensor_html_ad9c451d7aa04362e84e0d68d42a8867a"><div class="ttname"><a href="classnz_1_1data_1_1_mapped_tensor.html#ad9c451d7aa04362e84e0d68d42a8867a">nz::data::MappedTensor::requiresGrad</a></div><div class="ttdeci">bool requiresGrad() const noexcept</div><div class="ttdoc">Checks whether the MappedTensor requires gradient computation.</div><div class="ttdef"><b>Definition</b> <a href="#l00198">MappedTensor.cu:198</a></div></div>
<div class="ttc" id="aclassnz_1_1data_1_1_mapped_tensor_html_add503096ece511cc5b44f75271ff424d"><div class="ttname"><a href="classnz_1_1data_1_1_mapped_tensor.html#add503096ece511cc5b44f75271ff424d">nz::data::MappedTensor::setRequiresGrad</a></div><div class="ttdeci">void setRequiresGrad(bool requires_grad)</div><div class="ttdoc">Sets the gradient requirement flag for the MappedTensor and manages the associated gradient memory ac...</div><div class="ttdef"><b>Definition</b> <a href="#l00221">MappedTensor.cu:221</a></div></div>
<div class="ttc" id="aclassnz_1_1data_1_1_mapped_tensor_html_ae133d08d4505a30c6bd34e4a7a311f9e"><div class="ttname"><a href="classnz_1_1data_1_1_mapped_tensor.html#ae133d08d4505a30c6bd34e4a7a311f9e">nz::data::MappedTensor::syncData</a></div><div class="ttdeci">void syncData() const</div><div class="ttdoc">Synchronizes the tensor data by waiting for all CUDA stream write operations on it to finish.</div><div class="ttdef"><b>Definition</b> <a href="#l00603">MappedTensor.cu:603</a></div></div>
<div class="ttc" id="aclassnz_1_1data_1_1_mapped_tensor_html_aeb5887e43ba46f43ab2ed5ed78a62de1"><div class="ttname"><a href="classnz_1_1data_1_1_mapped_tensor.html#aeb5887e43ba46f43ab2ed5ed78a62de1">nz::data::MappedTensor::operator-</a></div><div class="ttdeci">MappedTensor operator-() const</div><div class="ttdoc">Perform element-wise negation on the MappedTensor.</div><div class="ttdef"><b>Definition</b> <a href="#l00467">MappedTensor.cu:467</a></div></div>
<div class="ttc" id="aclassnz_1_1data_1_1_mapped_tensor_html_aee0ad19fbaa29f4a89e99f21bdd4cdf0"><div class="ttname"><a href="classnz_1_1data_1_1_mapped_tensor.html#aee0ad19fbaa29f4a89e99f21bdd4cdf0">nz::data::MappedTensor::~MappedTensor</a></div><div class="ttdeci">~MappedTensor() noexcept(false)</div><div class="ttdoc">Destructor for the MappedTensor class.</div><div class="ttdef"><b>Definition</b> <a href="#l00179">MappedTensor.cu:179</a></div></div>
<div class="ttc" id="aclassnz_1_1data_1_1_mapped_tensor_html_aefb5c705fc6d224469978cf64ca37e0b"><div class="ttname"><a href="classnz_1_1data_1_1_mapped_tensor.html#aefb5c705fc6d224469978cf64ca37e0b">nz::data::MappedTensor::setShape</a></div><div class="ttdeci">void setShape(const shape_type &amp;shape)</div><div class="ttdoc">Sets a new shape for the MappedTensor and adjusts its data and gradient memory accordingly.</div><div class="ttdef"><b>Definition</b> <a href="#l00233">MappedTensor.cu:233</a></div></div>
<div class="ttc" id="aclassnz_1_1data_1_1_mapped_tensor_html_af9d1b0fa71f9bb7686cc26ae7b32be8d"><div class="ttname"><a href="classnz_1_1data_1_1_mapped_tensor.html#af9d1b0fa71f9bb7686cc26ae7b32be8d">nz::data::MappedTensor::syncGrad</a></div><div class="ttdeci">void syncGrad() const</div><div class="ttdoc">Synchronizes the gradient data if gradient computation is required.</div><div class="ttdef"><b>Definition</b> <a href="#l00597">MappedTensor.cu:597</a></div></div>
<div class="ttc" id="aclassnz_1_1data_1_1_mapped_tensor_html_afabf30bc35435f1101a61afa77b026ad"><div class="ttname"><a href="classnz_1_1data_1_1_mapped_tensor.html#afabf30bc35435f1101a61afa77b026ad">nz::data::MappedTensor::sum</a></div><div class="ttdeci">value_type sum() const</div><div class="ttdoc">Calculate the sum of all elements in the MappedTensor.</div><div class="ttdef"><b>Definition</b> <a href="#l00527">MappedTensor.cu:527</a></div></div>
<div class="ttc" id="aclassnz_1_1data_1_1_mapped_tensor_html_afae0e7076636cd708762f57f10f03952"><div class="ttname"><a href="classnz_1_1data_1_1_mapped_tensor.html#afae0e7076636cd708762f57f10f03952">nz::data::MappedTensor::transpose</a></div><div class="ttdeci">void transpose()</div><div class="ttdoc">Transpose the MappedTensor and its gradients (if required).</div><div class="ttdef"><b>Definition</b> <a href="#l00421">MappedTensor.cu:421</a></div></div>
<div class="ttc" id="anamespacenz_1_1data_html"><div class="ttname"><a href="namespacenz_1_1data.html">nz::data</a></div><div class="ttdoc">Contains data structures and utilities for tensor operations in machine learning workflows.</div><div class="ttdef"><b>Definition</b> <a href="_dimension_8cuh_source.html#l00009">Dimension.cuh:9</a></div></div>
<div class="ttc" id="anamespacenz_1_1data_html_a1da5cd018533919ed5a750b14c7d6d71"><div class="ttname"><a href="namespacenz_1_1data.html#a1da5cd018533919ed5a750b14c7d6d71">nz::data::tensorElementwiseDivide</a></div><div class="ttdeci">std::enable_if_t&lt; is_valid_tensor_type&lt; T &gt;::value, void &gt; tensorElementwiseDivide(T &amp;out, const T &amp;lhs, const T &amp;rhs)</div><div class="ttdoc">Performs element - wise division operation on tensors with broadcast compatibility.</div><div class="ttdef"><b>Definition</b> <a href="_tensor_operations_8cuh_source.html#l00928">TensorOperations.cuh:928</a></div></div>
<div class="ttc" id="anamespacenz_1_1data_html_a5a166a472b887c45fde9e5815f072234"><div class="ttname"><a href="namespacenz_1_1data.html#a5a166a472b887c45fde9e5815f072234">nz::data::tensorGeneralMatrixMul</a></div><div class="ttdeci">std::enable_if_t&lt; is_valid_tensor_type&lt; T &gt;::value, void &gt; tensorGeneralMatrixMul(T &amp;out, const T &amp;lhs, const T &amp;rhs)</div><div class="ttdoc">Performs general matrix multiplication on tensors with broadcast compatibility.</div><div class="ttdef"><b>Definition</b> <a href="_tensor_operations_8cuh_source.html#l01000">TensorOperations.cuh:1000</a></div></div>
<div class="ttc" id="anamespacenz_1_1data_html_a7503b6894e8052ed54eb169550d135c0"><div class="ttname"><a href="namespacenz_1_1data.html#a7503b6894e8052ed54eb169550d135c0">nz::data::tensorMatrixSub</a></div><div class="ttdeci">std::enable_if_t&lt; is_valid_tensor_type&lt; T &gt;::value, void &gt; tensorMatrixSub(T &amp;out, const T &amp;lhs, const T &amp;rhs)</div><div class="ttdoc">Performs matrix subtraction operation on tensors with broadcast compatibility.</div><div class="ttdef"><b>Definition</b> <a href="_tensor_operations_8cuh_source.html#l00858">TensorOperations.cuh:858</a></div></div>
<div class="ttc" id="anamespacenz_1_1data_html_a8cf4ac2437dd67698684169bebb225d4"><div class="ttname"><a href="namespacenz_1_1data.html#a8cf4ac2437dd67698684169bebb225d4">nz::data::tensorMatrixAdd</a></div><div class="ttdeci">std::enable_if_t&lt; is_valid_tensor_type&lt; T &gt;::value, void &gt; tensorMatrixAdd(T &amp;out, const T &amp;lhs, const T &amp;rhs)</div><div class="ttdoc">Performs matrix addition operation on tensors with broadcast compatibility.</div><div class="ttdef"><b>Definition</b> <a href="_tensor_operations_8cuh_source.html#l00787">TensorOperations.cuh:787</a></div></div>
<div class="ttc" id="anamespacenz_1_1krnl_html_a1ae846a65c2f5b83cd1b9fc61b877854"><div class="ttname"><a href="namespacenz_1_1krnl.html#a1ae846a65c2f5b83cd1b9fc61b877854">nz::krnl::Summation</a></div><div class="ttdeci">void Summation(dim3 gridDim, dim3 blockDim, unsigned long long sharedMemSize, float *out, float *in, unsigned long long n, size_t offset=0)</div><div class="ttdoc">Kernel function to perform element-wise summation of two arrays.</div><div class="ttdef"><b>Definition</b> <a href="_operation_kernels_8cu_source.html#l01225">OperationKernels.cu:1225</a></div></div>
<div class="ttc" id="anamespacenz_1_1krnl_html_a51a5ff3c8cc2c3051fddf32de294b467"><div class="ttname"><a href="namespacenz_1_1krnl.html#a51a5ff3c8cc2c3051fddf32de294b467">nz::krnl::SummationExp</a></div><div class="ttdeci">void SummationExp(dim3 gridDim, dim3 blockDim, size_t sharedMemSize, float *out, float *g_data, unsigned long long n, size_t offset=0)</div><div class="ttdoc">Kernel function to compute the summation of exponentials of each element in the input array.</div><div class="ttdef"><b>Definition</b> <a href="_operation_kernels_8cu_source.html#l00510">OperationKernels.cu:510</a></div></div>
<div class="ttc" id="anamespacenz_1_1krnl_html_ad136c8a6560a5305984ce0a31bea71bf"><div class="ttname"><a href="namespacenz_1_1krnl.html#ad136c8a6560a5305984ce0a31bea71bf">nz::krnl::Fill</a></div><div class="ttdeci">void Fill(dim3 gridDim, dim3 blockDim, float *data, float value, unsigned long long n, size_t offset=0)</div><div class="ttdoc">Kernel function to fill a data array with a given value.</div><div class="ttdef"><b>Definition</b> <a href="_operation_kernels_8cu_source.html#l01153">OperationKernels.cu:1153</a></div></div>
<div class="ttc" id="anamespacenz_1_1krnl_html_adc047e65307dbc711235f637227b7d10"><div class="ttname"><a href="namespacenz_1_1krnl.html#adc047e65307dbc711235f637227b7d10">nz::krnl::Recip</a></div><div class="ttdeci">void Recip(dim3 gridDim, dim3 blockDim, float *out, float *in, unsigned long long n)</div><div class="ttdoc">Kernel function to compute the reciprocal of each element of a matrix on the GPU.</div><div class="ttdef"><b>Definition</b> <a href="_operation_kernels_8cu_source.html#l00226">OperationKernels.cu:226</a></div></div>
<div class="ttc" id="anamespacenz_1_1krnl_html_af7069a420e81babb49b1bc009333d053"><div class="ttname"><a href="namespacenz_1_1krnl.html#af7069a420e81babb49b1bc009333d053">nz::krnl::Negation</a></div><div class="ttdeci">void Negation(dim3 gridDim, dim3 blockDim, float *out, float *in, unsigned long long n)</div><div class="ttdoc">Kernel function to negate each element of a matrix on the GPU.</div><div class="ttdef"><b>Definition</b> <a href="_operation_kernels_8cu_source.html#l00209">OperationKernels.cu:209</a></div></div>
<div class="ttc" id="anamespacenz_1_1krnl_html_afe3f38f788c735b7eb718443eb0fd094"><div class="ttname"><a href="namespacenz_1_1krnl.html#afe3f38f788c735b7eb718443eb0fd094">nz::krnl::Transpose</a></div><div class="ttdeci">void Transpose(dim3 gridDim, dim3 blockDim, float *d_A, float *d_B, unsigned int rows, unsigned int cols, size_t offset=0)</div><div class="ttdoc">Kernel function to transpose a matrix on the GPU.</div><div class="ttdef"><b>Definition</b> <a href="_operation_kernels_8cu_source.html#l00147">OperationKernels.cu:147</a></div></div>
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